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Model</title> <meta name="description" content="Search results for: predictive Model"> <meta name="keywords" content="predictive Model"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="predictive Model"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 17307</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: predictive Model</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17307</span> Metabolic Predictive Model for PMV Control Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eunji%20Choi">Eunji Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Borang%20Park"> Borang Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Youngjae%20Choi"> Youngjae Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinwoo%20Moon"> Jinwoo Moon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=indoor%20quality" title=" indoor quality"> indoor quality</a>, <a href="https://publications.waset.org/abstracts/search?q=metabolism" title=" metabolism"> metabolism</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20model" title=" predictive model"> predictive model</a> </p> <a href="https://publications.waset.org/abstracts/93271/metabolic-predictive-model-for-pmv-control-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93271.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">257</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17306</span> Application of Fractional Model Predictive Control to Thermal System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aymen%20Rhouma">Aymen Rhouma</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Hcheichi"> Khaled Hcheichi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sami%20Hafsi"> Sami Hafsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article presents an application of Fractional Model Predictive Control (FMPC) to a fractional order thermal system using Controlled Auto Regressive Integrated Moving Average (CARIMA) model obtained by discretization of a continuous fractional differential equation. Moreover, the output deviation approach is exploited to design the K -step ahead output predictor, and the corresponding control law is obtained by solving a quadratic cost function. Experiment results onto a thermal system are presented to emphasize the performances and the effectiveness of the proposed predictive controller<em>.</em> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractional%20model%20predictive%20control" title="fractional model predictive control">fractional model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20order%20systems" title=" fractional order systems"> fractional order systems</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20system" title=" thermal system"> thermal system</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20control" title=" predictive control"> predictive control</a> </p> <a href="https://publications.waset.org/abstracts/66187/application-of-fractional-model-predictive-control-to-thermal-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66187.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">411</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17305</span> Model Predictive Control of Three Phase Inverter for PV Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irtaza%20M.%20Syed">Irtaza M. Syed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaamran%20Raahemifar"> Kaamran Raahemifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a model predictive control (MPC) of a utility interactive three phase inverter (TPI) for a photovoltaic (PV) system at commercial level. The proposed model uses phase locked loop (PLL) to synchronize TPI with the power electric grid (PEG) and performs MPC control in a dq reference frame. TPI model consists of boost converter (BC), maximum power point tracking (MPPT) control, and a three leg voltage source inverter (VSI). Operational model of VSI is used to synthesize sinusoidal current and track the reference. Model is validated using a 35.7 kW PV system in Matlab/Simulink. Implementation and results show simplicity and accuracy, as well as reliability of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=three%20phase%20voltage%20source%20inverter" title=" three phase voltage source inverter"> three phase voltage source inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=PV%20system" title=" PV system"> PV system</a>, <a href="https://publications.waset.org/abstracts/search?q=Matlab%2Fsimulink" title=" Matlab/simulink"> Matlab/simulink</a> </p> <a href="https://publications.waset.org/abstracts/40124/model-predictive-control-of-three-phase-inverter-for-pv-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40124.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">596</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17304</span> Forecasting for Financial Stock Returns Using a Quantile Function Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuzhi%20Cai">Yuzhi Cai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce a newly developed quantile function model that can be used for estimating conditional distributions of financial returns and for obtaining multi-step ahead out-of-sample predictive distributions of financial returns. Since we forecast the whole conditional distributions, any predictive quantity of interest about the future financial returns can be obtained simply as a by-product of the method. We also show an application of the model to the daily closing prices of Dow Jones Industrial Average (DJIA) series over the period from 2 January 2004 - 8 October 2010. We obtained the predictive distributions up to 15 days ahead for the DJIA returns, which were further compared with the actually observed returns and those predicted from an AR-GARCH model. The results show that the new model can capture the main features of financial returns and provide a better fitted model together with improved mean forecasts compared with conventional methods. We hope this talk will help audience to see that this new model has the potential to be very useful in practice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DJIA" title="DJIA">DJIA</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20returns" title=" financial returns"> financial returns</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20distribution" title=" predictive distribution"> predictive distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20function%20model" title=" quantile function model"> quantile function model</a> </p> <a href="https://publications.waset.org/abstracts/33434/forecasting-for-financial-stock-returns-using-a-quantile-function-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33434.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17303</span> Computational Simulations on Stability of Model Predictive Control for Linear Discrete-Time Stochastic Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial time and a moving terminal time. This paper examines the stability of model predictive control for linear discrete-time systems with additive stochastic disturbances. A sufficient condition for the stability of the closed-loop system with model predictive control is derived by means of a linear matrix inequality. The objective of this paper is to show the results of computational simulations in order to verify the validity of the obtained stability condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computational%20simulations" title="computational simulations">computational simulations</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20control" title=" predictive control"> predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete-time%20systems" title=" discrete-time systems"> discrete-time systems</a> </p> <a href="https://publications.waset.org/abstracts/35462/computational-simulations-on-stability-of-model-predictive-control-for-linear-discrete-time-stochastic-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35462.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17302</span> Temperature Control Improvement of Membrane Reactor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pornsiri%20Kaewpradit">Pornsiri Kaewpradit</a>, <a href="https://publications.waset.org/abstracts/search?q=Chalisa%20Pourneaw"> Chalisa Pourneaw</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Temperature control improvement of a membrane reactor with exothermic and reversible esterification reaction is studied in this work. It is well known that a batch membrane reactor requires different control strategies from a continuous one due to the fact that it is operated dynamically. Due to the effect of the operating temperature, the suitable control scheme has to be designed based reliable predictive model to achieve a desired objective. In the study, the optimization framework has been preliminary formulated in order to determine an optimal temperature trajectory for maximizing a desired product. In model predictive control scheme, a set of predictive models have been initially developed corresponding to the possible operating points of the system. The multiple predictive control moves have been further calculated on-line using the developed models corresponding to current operating point. It is obviously seen in the simulation results that the temperature control has been improved compared to the performance obtained by the conventional predictive controller. Further robustness tests have also been investigated in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=batch%20reactor" title=" batch reactor"> batch reactor</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature%20control" title=" temperature control"> temperature control</a>, <a href="https://publications.waset.org/abstracts/search?q=membrane%20reactor" title=" membrane reactor "> membrane reactor </a> </p> <a href="https://publications.waset.org/abstracts/17487/temperature-control-improvement-of-membrane-reactor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17487.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">468</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17301</span> Model Predictive Control Using Thermal Inputs for Crystal Growth Dynamics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Shimizu">Takashi Shimizu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, crystal growth technologies have made progress by the requirement for the high quality of crystal materials. To control the crystal growth dynamics actively by external forces is useuful for reducing composition non-uniformity. In this study, a control method based on model predictive control using thermal inputs is proposed for crystal growth dynamics of semiconductor materials. The control system of crystal growth dynamics considered here is governed by the continuity, momentum, energy, and mass transport equations. To establish the control method for such thermal fluid systems, we adopt model predictive control known as a kind of optimal feedback control in which the control performance over a finite future is optimized with a performance index that has a moving initial time and terminal time. The objective of this study is to establish a model predictive control method for crystal growth dynamics of semiconductor materials. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title=" optimal control"> optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20control" title=" process control"> process control</a>, <a href="https://publications.waset.org/abstracts/search?q=crystal%20growth" title=" crystal growth"> crystal growth</a> </p> <a href="https://publications.waset.org/abstracts/88644/model-predictive-control-using-thermal-inputs-for-crystal-growth-dynamics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88644.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">359</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17300</span> Sampled-Data Model Predictive Tracking Control for Mobile Robot</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wookyong%20Kwon">Wookyong Kwon</a>, <a href="https://publications.waset.org/abstracts/search?q=Sangmoon%20Lee"> Sangmoon Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a sampled-data model predictive tracking control method is presented for mobile robots which is modeled as constrained continuous-time linear parameter varying (LPV) systems. The presented sampled-data predictive controller is designed by linear matrix inequality approach. Based on the input delay approach, a controller design condition is derived by constructing a new Lyapunov function. Finally, a numerical example is given to demonstrate the effectiveness of the presented method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=sampled-data%20control" title=" sampled-data control"> sampled-data control</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20parameter%20varying%20systems" title=" linear parameter varying systems"> linear parameter varying systems</a>, <a href="https://publications.waset.org/abstracts/search?q=LPV" title=" LPV"> LPV</a> </p> <a href="https://publications.waset.org/abstracts/71683/sampled-data-model-predictive-tracking-control-for-mobile-robot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71683.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">309</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17299</span> Space Vector PWM and Model Predictive Control for Voltage Source Inverter Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irtaza%20M.%20Syed">Irtaza M. Syed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaamran%20Raahemifar"> Kaamran Raahemifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a comparative assessment of Space Vector Pulse Width Modulation (SVPWM) and Model Predictive Control (MPC) for two-level three phase (2L-3P) Voltage Source Inverter (VSI). VSI with associated system is subjected to both control techniques and the results are compared. Matlab/Simulink was used to model, simulate and validate the control schemes. Findings of this study show that MPC is superior to SVPWM in terms of total harmonic distortion (THD) and implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=voltage%20source%20inverter" title="voltage source inverter">voltage source inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=space%20vector%20pulse%20width%20modulation" title=" space vector pulse width modulation"> space vector pulse width modulation</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=comparison" title=" comparison"> comparison</a> </p> <a href="https://publications.waset.org/abstracts/16220/space-vector-pwm-and-model-predictive-control-for-voltage-source-inverter-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16220.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">508</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17298</span> Systematic and Simple Guidance for Feed Forward Design in Model Predictive Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shukri%20Dughman">Shukri Dughman</a>, <a href="https://publications.waset.org/abstracts/search?q=Anthony%20Rossiter"> Anthony Rossiter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper builds on earlier work which demonstrated that Model Predictive Control (MPC) may give a poor choice of default feed forward compensator. By first demonstrating the impact of future information of target changes on the performance, this paper proposes a pragmatic method for identifying the amount of future information on the target that can be utilised effectively in both finite and infinite horizon algorithms. Numerical illustrations in MATLAB give evidence of the efficacy of the proposal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking%20control" title=" tracking control"> tracking control</a>, <a href="https://publications.waset.org/abstracts/search?q=advance%20knowledge" title=" advance knowledge"> advance knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=feed%20forward" title=" feed forward"> feed forward</a> </p> <a href="https://publications.waset.org/abstracts/36567/systematic-and-simple-guidance-for-feed-forward-design-in-model-predictive-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36567.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">547</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17297</span> Evaluating the Diagnostic Accuracy of the ctDNA Methylation for Liver Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maomao%20Cao">Maomao Cao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Objective: To test the performance of ctDNA methylation for the detection of liver cancer. Methods: A total of 1233 individuals have been recruited in 2017. 15 male and 15 female samples (including 10 cases of liver cancer) were randomly selected in the present study. CfDNA was extracted by MagPure Circulating DNA Maxi Kit. The concentration of cfDNA was obtained by Qubit™ dsDNA HS Assay Kit. A pre-constructed predictive model was used to analyze methylation data and to give a predictive score for each cfDNA sample. Individuals with a predictive score greater than or equal to 80 were classified as having liver cancer. CT tests were considered the gold standard. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the diagnosis of liver cancer were calculated. Results: 9 patients were diagnosed with liver cancer according to the prediction model (with high sensitivity and threshold of 80 points), with scores of 99.2, 91.9, 96.6, 92.4, 91.3, 92.5, 96.8, 91.1, and 92.2, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of ctDNA methylation for the diagnosis of liver cancer were 0.70, 0.90, 0.78, and 0.86, respectively. Conclusions: ctDNA methylation could be an acceptable diagnostic modality for the detection of liver cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=liver%20cancer" title="liver cancer">liver cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=ctDNA%20methylation" title=" ctDNA methylation"> ctDNA methylation</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnostic%20performance" title=" diagnostic performance"> diagnostic performance</a> </p> <a href="https://publications.waset.org/abstracts/146512/evaluating-the-diagnostic-accuracy-of-the-ctdna-methylation-for-liver-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146512.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">151</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17296</span> Model Predictive Control with Unscented Kalman Filter for Nonlinear Implicit Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takashi%20Shimizu">Takashi Shimizu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A class of implicit systems is known as a more generalized class of systems than a class of explicit systems. To establish a control method for such a generalized class of systems, we adopt model predictive control method which is a kind of optimal feedback control with a performance index that has a moving initial time and terminal time. However, model predictive control method is inapplicable to systems whose all state variables are not exactly known. In other words, model predictive control method is inapplicable to systems with limited measurable states. In fact, it is usual that the state variables of systems are measured through outputs, hence, only limited parts of them can be used directly. It is also usual that output signals are disturbed by process and sensor noises. Hence, it is important to establish a state estimation method for nonlinear implicit systems with taking the process noise and sensor noise into consideration. To this purpose, we apply the model predictive control method and unscented Kalman filter for solving the optimization and estimation problems of nonlinear implicit systems, respectively. The objective of this study is to establish a model predictive control with unscented Kalman filter for nonlinear implicit systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20systems" title=" nonlinear systems"> nonlinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimation" title=" state estimation"> state estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a> </p> <a href="https://publications.waset.org/abstracts/97739/model-predictive-control-with-unscented-kalman-filter-for-nonlinear-implicit-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97739.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">202</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17295</span> Collision Avoidance Based on Model Predictive Control for Nonlinear Octocopter Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Do%C4%9Fan%20Y%C4%B1ld%C4%B1z">Doğan Yıldız</a>, <a href="https://publications.waset.org/abstracts/search?q=Aydan%20M%C3%BC%C5%9Ferref%20Erkmen"> Aydan Müşerref Erkmen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The controller of the octocopter is mostly based on the PID controller. For complex maneuvers, PID controllers have limited performance capability like in collision avoidance. When an octocopter needs avoidance from an obstacle, it must instantly show an agile maneuver. Also, this kind of maneuver is affected severely by the nonlinear characteristic of octocopter. When these kinds of limitations are considered, the situation is highly challenging for the PID controller. In the proposed study, these challenges are tried to minimize by using the model predictive controller (MPC) for collision avoidance with a nonlinear octocopter model. The aim is to show that MPC-based collision avoidance has the capability to deal with fast varying conditions in case of obstacle detection and diminish the nonlinear effects of octocopter with varying disturbances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20octocopter%20model" title=" nonlinear octocopter model"> nonlinear octocopter model</a>, <a href="https://publications.waset.org/abstracts/search?q=collision%20avoidance" title=" collision avoidance"> collision avoidance</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle%20detection" title=" obstacle detection"> obstacle detection</a> </p> <a href="https://publications.waset.org/abstracts/150063/collision-avoidance-based-on-model-predictive-control-for-nonlinear-octocopter-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150063.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">191</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17294</span> A Predictive Model of Supply and Demand in the State of Jalisco, Mexico</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Gil">M. Gil</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Montalvo"> R. Montalvo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Business Intelligence (BI) has become a major source of competitive advantages for firms around the world. BI has been defined as the process of data visualization and reporting for understanding what happened and what is happening. Moreover, BI has been studied for its predictive capabilities in the context of trade and financial transactions. The current literature has identified that BI permits managers to identify market trends, understand customer relations, and predict demand for their products and services. This last capability of BI has been of special concern to academics. Specifically, due to its power to build predictive models adaptable to specific time horizons and geographical regions. However, the current literature of BI focuses on predicting specific markets and industries because the impact of such predictive models was relevant to specific industries or organizations. Currently, the existing literature has not developed a predictive model of BI that takes into consideration the whole economy of a geographical area. This paper seeks to create a predictive model of BI that would show the bigger picture of a geographical area. This paper uses a data set from the Secretary of Economic Development of the state of Jalisco, Mexico. Such data set includes data from all the commercial transactions that occurred in the state in the last years. By analyzing such data set, it will be possible to generate a BI model that predicts supply and demand from specific industries around the state of Jalisco. This research has at least three contributions. Firstly, a methodological contribution to the BI literature by generating the predictive supply and demand model. Secondly, a theoretical contribution to BI current understanding. The model presented in this paper incorporates the whole picture of the economic field instead of focusing on a specific industry. Lastly, a practical contribution might be relevant to local governments that seek to improve their economic performance by implementing BI in their policy planning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title="business intelligence">business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20model" title=" predictive model"> predictive model</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20and%20demand" title=" supply and demand"> supply and demand</a>, <a href="https://publications.waset.org/abstracts/search?q=Mexico" title=" Mexico"> Mexico</a> </p> <a href="https://publications.waset.org/abstracts/123714/a-predictive-model-of-supply-and-demand-in-the-state-of-jalisco-mexico" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123714.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">123</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17293</span> A Predictive Machine Learning Model of the Survival of Female-led and Co-Led Small and Medium Enterprises in the UK</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mais%20Khader">Mais Khader</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingjie%20Wei"> Xingjie Wei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research sheds light on female entrepreneurs by providing new insights on the survival predictions of companies led by females in the UK. This study aims to build a predictive machine learning model of the survival of female-led & co-led small & medium enterprises (SMEs) in the UK over the period 2000-2020. The predictive model built utilised a combination of financial and non-financial features related to both companies and their directors to predict SMEs' survival. These features were studied in terms of their contribution to the resultant predictive model. Five machine learning models are used in the modelling: Decision tree, AdaBoost, Naïve Bayes, Logistic regression and SVM. The AdaBoost model had the highest performance of the five models, with an accuracy of 73% and an AUC of 80%. The results show high feature importance in predicting companies' survival for company size, management experience, financial performance, industry, region, and females' percentage in management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=company%20survival" title="company survival">company survival</a>, <a href="https://publications.waset.org/abstracts/search?q=entrepreneurship" title=" entrepreneurship"> entrepreneurship</a>, <a href="https://publications.waset.org/abstracts/search?q=females" title=" females"> females</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=SMEs" title=" SMEs"> SMEs</a> </p> <a href="https://publications.waset.org/abstracts/172021/a-predictive-machine-learning-model-of-the-survival-of-female-led-and-co-led-small-and-medium-enterprises-in-the-uk" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172021.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17292</span> Online Robust Model Predictive Control for Linear Fractional Transformation Systems Using Linear Matrix Inequalities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peyman%20Sindareh%20Esfahani">Peyman Sindareh Esfahani</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeffery%20Kurt%20Pieper"> Jeffery Kurt Pieper</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the problem of robust model predictive control (MPC) for discrete-time linear systems in linear fractional transformation form with structured uncertainty and norm-bounded disturbance is investigated. The problem of minimization of the cost function for MPC design is converted to minimization of the worst case of the cost function. Then, this problem is reduced to minimization of an upper bound of the cost function subject to a terminal inequality satisfying the <em>l</em><sub>2</sub>-norm of the closed loop system. The characteristic of the linear fractional transformation system is taken into account, and by using some mathematical tools, the robust predictive controller design problem is turned into a linear matrix inequality minimization problem. Afterwards, a formulation which includes an integrator to improve the performance of the proposed robust model predictive controller in steady state condition is studied. The validity of the approaches is illustrated through a robust control benchmark problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=linear%20fractional%20transformation" title="linear fractional transformation">linear fractional transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20matrix%20inequality" title=" linear matrix inequality"> linear matrix inequality</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20model%20predictive%20control" title=" robust model predictive control"> robust model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20feedback%20control" title=" state feedback control"> state feedback control</a> </p> <a href="https://publications.waset.org/abstracts/69466/online-robust-model-predictive-control-for-linear-fractional-transformation-systems-using-linear-matrix-inequalities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69466.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">395</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17291</span> The Extended Skew Gaussian Process for Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20T.%20Alodat">M. T. Alodat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a generalization to the Gaussian process regression(GPR) model called the extended skew Gaussian process for regression(ESGPr) model. The ESGPR model works better than the GPR model when the errors are skewed. We derive the predictive distribution for the ESGPR model at a new input. Also we apply the ESGPR model to FOREX data and we find that it fits the Forex data better than the GPR model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extended%20skew%20normal%20distribution" title="extended skew normal distribution">extended skew normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20for%20regression" title=" Gaussian process for regression"> Gaussian process for regression</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20distribution" title=" predictive distribution"> predictive distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=ESGPr%20model" title=" ESGPr model"> ESGPr model</a> </p> <a href="https://publications.waset.org/abstracts/2233/the-extended-skew-gaussian-process-for-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2233.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">553</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17290</span> RBF Modelling and Optimization Control for Semi-Batch Reactors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Magdi%20M.%20Nabi">Magdi M. Nabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ding-Li%20Yu"> Ding-Li Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chylla-Haase%20reactor" title="Chylla-Haase reactor">Chylla-Haase reactor</a>, <a href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20network%20modelling" title=" RBF neural network modelling"> RBF neural network modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-batch%20reactors" title=" semi-batch reactors"> semi-batch reactors</a> </p> <a href="https://publications.waset.org/abstracts/11884/rbf-modelling-and-optimization-control-for-semi-batch-reactors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11884.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">468</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17289</span> Model Predictive Controller for Pasteurization Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tesfaye%20Alamirew%20Dessie">Tesfaye Alamirew Dessie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our study focuses on developing a Model Predictive Controller (MPC) and evaluating it against a traditional PID for a pasteurization process. Utilizing system identification from the experimental data, the dynamics of the pasteurization process were calculated. Using best fit with data validation, residual, and stability analysis, the quality of several model architectures was evaluated. The validation data fit the auto-regressive with exogenous input (ARX322) model of the pasteurization process by roughly 80.37 percent. The ARX322 model structure was used to create MPC and PID control techniques. After comparing controller performance based on settling time, overshoot percentage, and stability analysis, it was found that MPC controllers outperform PID for those parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MPC" title="MPC">MPC</a>, <a href="https://publications.waset.org/abstracts/search?q=PID" title=" PID"> PID</a>, <a href="https://publications.waset.org/abstracts/search?q=ARX" title=" ARX"> ARX</a>, <a href="https://publications.waset.org/abstracts/search?q=pasteurization" title=" pasteurization"> pasteurization</a> </p> <a href="https://publications.waset.org/abstracts/154469/model-predictive-controller-for-pasteurization-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154469.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17288</span> Numerical Simulations on Feasibility of Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taiki%20Baba">Taiki Baba</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto"> Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The random dither quantization method enables us to achieve much better performance than the simple uniform quantization method for the design of quantized control systems. Motivated by this fact, the stochastic model predictive control method in which a performance index is minimized subject to probabilistic constraints imposed on the state variables of systems has been proposed for linear feedback control systems with random dither quantization. In other words, a method for solving optimal control problems subject to probabilistic state constraints for linear discrete-time control systems with random dither quantization has been already established. To our best knowledge, however, the feasibility of such a kind of optimal control problems has not yet been studied. Our objective in this paper is to investigate the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. To this end, we provide the results of numerical simulations that verify the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20constraints" title=" probabilistic constraints"> probabilistic constraints</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20dither%20quantization" title=" random dither quantization"> random dither quantization</a> </p> <a href="https://publications.waset.org/abstracts/78538/numerical-simulations-on-feasibility-of-stochastic-model-predictive-control-for-linear-discrete-time-systems-with-random-dither-quantization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78538.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">282</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17287</span> Model Predictive Control of Turbocharged Diesel Engine with Exhaust Gas Recirculation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.%20Yavas">U. Yavas</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gokasan"> M. Gokasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control of diesel engine’s air path has drawn a lot of attention due to its multi input-multi output, closed coupled, non-linear relation. Today, precise control of amount of air to be combusted is a must in order to meet with tight emission limits and performance targets. In this study, passenger car size diesel engine is modeled by AVL Boost RT, and then simulated with standard, industry level PID controllers. Finally, linear model predictive control is designed and simulated. This study shows the importance of modeling and control of diesel engines with flexible algorithm development in computer based systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20control" title="predictive control">predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=engine%20control" title=" engine control"> engine control</a>, <a href="https://publications.waset.org/abstracts/search?q=engine%20modeling" title=" engine modeling"> engine modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20control" title=" PID control"> PID control</a>, <a href="https://publications.waset.org/abstracts/search?q=feedforward%20compensation" title=" feedforward compensation"> feedforward compensation</a> </p> <a href="https://publications.waset.org/abstracts/34455/model-predictive-control-of-turbocharged-diesel-engine-with-exhaust-gas-recirculation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34455.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">636</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17286</span> Use of Predictive Food Microbiology to Determine the Shelf-Life of Foods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatih%20Tarlak">Fatih Tarlak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predictive microbiology can be considered as an important field in food microbiology in which it uses predictive models to describe the microbial growth in different food products. Predictive models estimate the growth of microorganisms quickly, efficiently, and in a cost-effective way as compared to traditional methods of enumeration, which are long-lasting, expensive, and time-consuming. The mathematical models used in predictive microbiology are mainly categorised as primary and secondary models. The primary models are the mathematical equations that define the growth data as a function of time under a constant environmental condition. The secondary models describe the effects of environmental factors, such as temperature, pH, and water activity (aw) on the parameters of the primary models, including the maximum specific growth rate and lag phase duration, which are the most critical growth kinetic parameters. The combination of primary and secondary models provides valuable information to set limits for the quantitative detection of the microbial spoilage and assess product shelf-life. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=shelf-life" title="shelf-life">shelf-life</a>, <a href="https://publications.waset.org/abstracts/search?q=growth%20model" title=" growth model"> growth model</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20microbiology" title=" predictive microbiology"> predictive microbiology</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/133723/use-of-predictive-food-microbiology-to-determine-the-shelf-life-of-foods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133723.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">211</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17285</span> Synthesis of a Model Predictive Controller for Artificial Pancreas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20El%20Hachimi">Mohamed El Hachimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhakim%20Ballouk"> Abdelhakim Ballouk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilyas%20%20Khelafa"> Ilyas Khelafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelaziz%20Mouhou"> Abdelaziz Mouhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Type 1 diabetes occurs when beta cells are destroyed by the body's own immune system. Treatment of type 1 diabetes mellitus could be greatly improved by applying a closed-loop control strategy to insulin delivery, also known as an Artificial Pancreas (AP). Method: In this paper, we present a new formulation of the cost function for a Model Predictive Control (MPC) utilizing a technic which accelerates the speed of control of the AP and tackles the nonlinearity of the control problem via asymmetric objective functions. Finding: The finding of this work consists in a new Model Predictive Control algorithm that leads to good performances like decreasing the time of hyperglycaemia and avoiding hypoglycaemia. Conclusion: These performances are validated under in silico trials. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20pancreas" title="artificial pancreas">artificial pancreas</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20algorithm" title=" control algorithm"> control algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=biomedical%20control" title=" biomedical control"> biomedical control</a>, <a href="https://publications.waset.org/abstracts/search?q=MPC" title=" MPC"> MPC</a>, <a href="https://publications.waset.org/abstracts/search?q=objective%20function" title=" objective function"> objective function</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinearity" title=" nonlinearity"> nonlinearity</a> </p> <a href="https://publications.waset.org/abstracts/69505/synthesis-of-a-model-predictive-controller-for-artificial-pancreas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69505.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">307</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17284</span> Predictive Models of Ruin Probability in Retirement Withdrawal Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuanjin%20Liu">Yuanjin Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Retirement withdrawal strategies are very important to minimize the probability of ruin in retirement. The ruin probability is modeled as a function of initial withdrawal age, gender, asset allocation, inflation rate, and initial withdrawal rate. The ruin probability is obtained based on the 2019 period life table for the Social Security, IRS Required Minimum Distribution (RMD) Worksheets, US historical bond and equity returns, and inflation rates using simulation. Several popular machine learning algorithms of the generalized additive model, random forest, support vector machine, extreme gradient boosting, and artificial neural network are built. The model validation and selection are based on the test errors using hyperparameter tuning and train-test split. The optimal model is recommended for retirees to monitor the ruin probability. The optimal withdrawal strategy can be obtained based on the optimal predictive model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ruin%20probability" title="ruin probability">ruin probability</a>, <a href="https://publications.waset.org/abstracts/search?q=retirement%20withdrawal%20strategies" title=" retirement withdrawal strategies"> retirement withdrawal strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20models" title=" predictive models"> predictive models</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20model" title=" optimal model"> optimal model</a> </p> <a href="https://publications.waset.org/abstracts/147438/predictive-models-of-ruin-probability-in-retirement-withdrawal-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147438.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">74</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17283</span> Nonlinear Model Predictive Control of Water Quality in Drinking Water Distribution Systems with DBPs Objetives</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mingyu%20Xie">Mingyu Xie</a>, <a href="https://publications.waset.org/abstracts/search?q=Mietek%20Brdys"> Mietek Brdys</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper develops a non-linear model predictive control (NMPC) of water quality in drinking water distribution systems (DWDS) based on the advanced non-linear quality dynamics model including disinfections by-products (DBPs). A special attention is paid to the analysis of an impact of the flow trajectories prescribed by an upper control level of the recently developed two-time scale architecture of an integrated quality and quantity control in DWDS. The new quality controller is to operate within this architecture in the fast time scale as the lower level quality controller. The controller performance is validated by a comprehensive simulation study based on an example case study DWDS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20control%20structure" title=" hierarchical control structure"> hierarchical control structure</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20with%20DBPs%20objectives" title=" water quality with DBPs objectives"> water quality with DBPs objectives</a> </p> <a href="https://publications.waset.org/abstracts/32624/nonlinear-model-predictive-control-of-water-quality-in-drinking-water-distribution-systems-with-dbps-objetives" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32624.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">317</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17282</span> Predictive Analysis for Big Data: Extension of Classification and Regression Trees Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ameur%20Abdelkader">Ameur Abdelkader</a>, <a href="https://publications.waset.org/abstracts/search?q=Abed%20Bouarfa%20Hafida"> Abed Bouarfa Hafida</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=predictive%20analysis" title="predictive analysis">predictive analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20analysis%20algorithms" title=" predictive analysis algorithms"> predictive analysis algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=CART%20algorithm" title=" CART algorithm"> CART algorithm</a> </p> <a href="https://publications.waset.org/abstracts/101647/predictive-analysis-for-big-data-extension-of-classification-and-regression-trees-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101647.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17281</span> Cognitive Footprints: Analytical and Predictive Paradigm for Digital Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marina%20Vicario">Marina Vicario</a>, <a href="https://publications.waset.org/abstracts/search?q=Amadeo%20Arg%C3%BCelles"> Amadeo Argüelles</a>, <a href="https://publications.waset.org/abstracts/search?q=Pilar%20G%C3%B3mez"> Pilar Gómez</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Hern%C3%A1ndez"> Carlos Hernández</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the Computer Research Network of the National Polytechnic Institute of Mexico proposes a paradigmatic model for the inference of cognitive patterns in digital learning systems. This model leads to metadata architecture useful for analysis and prediction in online learning systems; especially on MOOc's architectures. The model is in the design phase and expects to be tested through an institutional of courses project which is going to develop for the MOOc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20footprints" title="cognitive footprints">cognitive footprints</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title=" learning analytics"> learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20learning" title=" predictive learning"> predictive learning</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20learning" title=" digital learning"> digital learning</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20computing" title=" educational computing"> educational computing</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20informatics" title=" educational informatics"> educational informatics</a> </p> <a href="https://publications.waset.org/abstracts/29913/cognitive-footprints-analytical-and-predictive-paradigm-for-digital-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29913.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">477</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17280</span> Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, feedback control systems using random dither quantizers have been proposed for linear discrete-time systems. However, the constraints imposed on state and control variables have not yet been taken into account for the design of feedback control systems with random dither quantization. Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial and terminal time. An important advantage of model predictive control is its ability to handle constraints imposed on state and control variables. Based on the model predictive control approach, the objective of this paper is to present a control method that satisfies probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization. In other words, this paper provides a method for solving the optimal control problems subject to probabilistic state constraints for linear discrete-time feedback control systems with random dither quantization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20dither" title=" random dither"> random dither</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a> </p> <a href="https://publications.waset.org/abstracts/63970/stochastic-model-predictive-control-for-linear-discrete-time-systems-with-random-dither-quantization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63970.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17279</span> A Model Predictive Control Based Virtual Active Power Filter Using V2G Technology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Zolfaghari">Mahdi Zolfaghari</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Hossein%20Hosseinian"> Seyed Hossein Hosseinian</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Askarian%20Abyaneh"> Hossein Askarian Abyaneh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehrdad%20Abedi"> Mehrdad Abedi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a virtual active power filter (VAPF) using vehicle to grid (V2G) technology to maintain power quality requirements. The optimal discrete operation of the power converter of electric vehicle (EV) is based on recognizing desired switching states using the model predictive control (MPC) algorithm. A fast dynamic response, lower total harmonic distortion (THD) and good reference tracking performance are realized through the presented control strategy. The simulation results using MATLAB/Simulink validate the effectiveness of the scheme in improving power quality as well as good dynamic response in power transferring capability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electric%20vehicle" title="electric vehicle">electric vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20quality" title=" power quality"> power quality</a>, <a href="https://publications.waset.org/abstracts/search?q=V2G%20technology" title=" V2G technology"> V2G technology</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20active%20power%20filter" title=" virtual active power filter"> virtual active power filter</a> </p> <a href="https://publications.waset.org/abstracts/70930/a-model-predictive-control-based-virtual-active-power-filter-using-v2g-technology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70930.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">430</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17278</span> MPC of Single Phase Inverter for PV System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irtaza%20M.%20Syed">Irtaza M. Syed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaamran%20Raahemifar"> Kaamran Raahemifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a model predictive control (MPC) of a utility interactive (UI) single phase inverter (SPI) for a photovoltaic (PV) system at residential/distribution level. The proposed model uses single-phase phase locked loop (PLL) to synchronize SPI with the grid and performs MPC control in a dq reference frame. SPI model consists of boost converter (BC), maximum power point tracking (MPPT) control, and a full bridge (FB) voltage source inverter (VSI). No PI regulators to tune and carrier and modulating waves are required to produce switching sequence. Instead, the operational model of VSI is used to synthesize sinusoidal current and track the reference. Model is validated using a three kW PV system at the input of UI-SPI in Matlab/Simulink. Implementation and results demonstrate simplicity and accuracy, as well as reliability of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=phase%20locked%20loop" title="phase locked loop">phase locked loop</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20source%20inverter" title=" voltage source inverter"> voltage source inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20phase%20inverter" title=" single phase inverter"> single phase inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=Matlab%2FSimulink" title=" Matlab/Simulink"> Matlab/Simulink</a> </p> <a href="https://publications.waset.org/abstracts/16006/mpc-of-single-phase-inverter-for-pv-system" class="btn btn-primary 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