18 results on '"Mudassir Rashid"'
Search Results
2. Latent Variables Model Based MPC for People with Type 1 Diabetes
- Author
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Mudassir Rashid, Xiaoyu Sun, Ali Cinar, and Mohammad Reza Askari
- Subjects
Model predictive control ,Type 1 diabetes ,Autoregressive model ,Control and Systems Engineering ,Control theory ,Noise (signal processing) ,Partial least squares regression ,medicine ,Range (statistics) ,Latent variable ,medicine.disease ,Mathematics - Abstract
A model predictive control (MPC) system based on latent variables (LV) model generated by using partial least squares (PLS) method is developed. The difference in the performance of MPCs that use recursively updated LV models based on autoregressive time series modeling (with exogenous inputs - ARX) and PLS is studied. The effect of signal noise on MPC performance is also investigated for both types of models. MPC performance is evaluated by regulating the blood glucose concentration (BGC) of people with Type 1 diabetes mellitus (T1DM) in simulation studies. Signal noise in glucose concentration sensor data, delays caused by insulin absorption and action, and disturbances caused by consumption of meals make the regulation of BGC difficult. The proposed controller is evaluated with 10 in-silico adult subjects of the UVa/Padova simulator with different levels of signal noise. The results illustrate the effectiveness of the MPC based on LV model. The average time for BGC in the safe range (70-180 mg/dL) for the LV-based MPC is 83.23% compared to 79.68% for the MPC based on ARX model when intravenous BGC values are used. The average time in safe range decreases to 76.04% and 71.92%, respectively, when using the generic CGM sensor of the simulator. It is reduced further to 71.93% and 67.20% when additional noise is added to CGM readings.
- Published
- 2021
3. Critical Quality Predictive Control of Fed-Batch Mammalian Cell Bioreactors
- Author
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Satish J. Parulekar, Ali Cinar, Robert Jackson, and Mudassir Rashid
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Tracking error ,Model predictive control ,Nonlinear system ,Noise ,Control and Systems Engineering ,Control theory ,Computer science ,Bioreactor ,Critical to quality ,Process (computing) ,Subspace topology - Abstract
A model predictive control (MPC) formulation for a mammalian cell fed-batch bioreactor processes is developed. A nonlinear fundamental model for the bioreactor is used to generate a database of historical runs comprising of the measurement variables and the manipulated input feed flow rate to the bioreactor. The database is used with subspace identification methods to develop a state-space model of the process. The identified model is used to design various MPC formulations with different objective criteria, including the conventional trajectory-tracking objective function and a novel terminal objective for maximizing the product yield at completion of a run. Case studies involving the simulated bioreactor process demonstrate the efficacy of the MPC algorithms subject to unknown disturbances, random variations in the inlet feed glucose and glutamine concentrations, and measurement noise. Compared to the traditional proportional-integral control algorithm, the trajectory-tracking predictive control algorithm is able to better track the reference glucose concentration set-point with an improvement of 5.1% in the tracking error. The critical quality attribute predictive control algorithm designed to maximize the product yield results in a 3.9% increase in the product concentration at the completion of the run.
- Published
- 2021
4. Adaptive-learning model predictive control for complex physiological systems: Automated insulin delivery in diabetes
- Author
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Ali Cinar, Victor M. Zavala, Nicole Hobbs, Iman Hajizadeh, Mohammad Reza Askari, and Mudassir Rashid
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0209 industrial biotechnology ,Computer science ,020208 electrical & electronic engineering ,02 engineering and technology ,Latent variable ,Regression ,Model predictive control ,020901 industrial engineering & automation ,Control and Systems Engineering ,Control theory ,Robustness (computer science) ,0202 electrical engineering, electronic engineering, information engineering ,Adaptive learning ,Uncertainty quantification ,Cluster analysis ,Software - Abstract
An adaptive-learning model predictive control (AL-MPC) framework is proposed for incorporating disturbance prediction, model uncertainty quantification, pattern learning, and recursive subspace identification for use in controlling complex dynamic systems with periodically recurring large random disturbances. The AL-MPC integrates online learning from historical data to predict the future evolution of the model output over a specified horizon and proactively mitigate significant disturbances. This goal is accomplished using dynamic regularized latent variable regression (DrLVR) approach to quantify disturbances from the past data and forecast their future progression time series. An enveloped path for the future behavior of the model output is extracted to further enhance the robustness of the closed-loop system. The controller set-point, penalty weights of the objective function, and constraints criteria can be modified in advance for the expected periods of the disturbance effects. The proposed AL-MPC is used to regulate glucose concentration in people with Type 1 diabetes by an automated insulin delivery system. Simulation results demonstrate the effectiveness of the proposed technique by improving the performance indices of the closed-loop system. The MPC algorithm integrated with DrLVR disturbance predictor has compared to MPC reinforced with dynamic principal component analysis linked with K-nearest neighbors and hyper-spherical clustering (k-means) technique. The simulation results illustrate that the AL-MPC can regulate the glucose concentrations of people with Type 1 diabetes to stay in the desired range (70–180) mg/dL 84.4% of the time without causing any hypoglycemia and hyperglycemia events.
- Published
- 2020
5. Application of Neural Networks for Heart Rate Monitoring
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Nichole Hobbs, Iman Hajizadeh, Mohammad Reza Askari, Mudassir Rashid, Ali Cinar, Xiaoyu Sun, Mert Sevil, and Rachel Brandt
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0209 industrial biotechnology ,Artifact (error) ,Artificial neural network ,business.industry ,Computer science ,020208 electrical & electronic engineering ,Feature extraction ,Pattern recognition ,02 engineering and technology ,Kalman filter ,Accelerometer ,Signal ,020901 industrial engineering & automation ,Band-pass filter ,Control and Systems Engineering ,0202 electrical engineering, electronic engineering, information engineering ,Artificial intelligence ,business ,Singular spectrum analysis - Abstract
This paper addresses the problem of heart rate (HR) monitoring from photo-plethysmography(PPG) sensors, where artifacts caused by body movements drastically affect the quality of the measurement signal. The PPG signal is windowed into consecutive segments, and for each time-windows, a Butterworth bandpass filter is utilized to attenuate high-frequency noises. Then, the PPG signal is processed by using the singular spectrum analysis technique to obtain a smooth PPG signal. In order to remove artifacts caused by the physical activity of the subject, the 3-dimensional accelerometer signal is used as an auxiliary signal to detect the presence of motion artifact (MA). A new spectral subtraction approach is proposed for MA rejection. For the purpose of HR estimation from the PPG signal, a feature extraction method is performed, and neural network binary classifier is used to detect the most probable frequencies corresponding to the actual HR. HR estimations are passed through a Kalman filter to result in smooth and accurate HR estimations.
- Published
- 2020
6. Virtual Patients: An Enabling Technology for Multivariable Control of Biomedical Systems
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Nicole Hobbs, Mudassir Rashid, Laurie Quinn, Minsun Park, Sediqeh Samadi, Mert Sevil, and Ali Cinar
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0209 industrial biotechnology ,education.field_of_study ,Adaptive control ,Computer science ,Multivariable calculus ,020208 electrical & electronic engineering ,Population ,Experimental data ,Sampling (statistics) ,Multivariate normal distribution ,02 engineering and technology ,computer.software_genre ,020901 industrial engineering & automation ,Virtual patient ,Control and Systems Engineering ,0202 electrical engineering, electronic engineering, information engineering ,Data mining ,education ,Cluster analysis ,computer - Abstract
This paper presents the development of virtual patients to enable the simulation evaluation and assessment of multivariable control algorithms for biomedical systems. The virtual patients are generated by fitting the parameters of the models to clinical experimental data, followed by the estimation of the multivariate distribution of the actual patient parameters. The estimated multivariate distribution is then incorporated with constraints to ensure the sampling of synthetic virtual patients conforms to the actual patient parameter bounds. The sampled synthetic virtual patients are analyzed through multivariate statistical techniques and data clustering algorithms to prune out virtual subjects with similar characteristics or unrealistic dynamics, yielding a virtual patient population that is diverse and with individually distinct characteristics. The generated virtual patient population is used to evaluate multivariable nonlinear and adaptive control algorithms for insulin dosing in people with Type 1 diabetes.
- Published
- 2020
7. Adaptive personalized prior-knowledge-informed model predictive control for type 1 diabetes
- Author
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Xiaoyu Sun, Mudassir Rashid, Mohammad Reza Askari, and Ali Cinar
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Control and Systems Engineering ,Applied Mathematics ,Electrical and Electronic Engineering ,Computer Science Applications - Abstract
This work considers the problem of adaptive prior-informed model predictive control (MPC) formulations that explicitly incorporate prior knowledge in the model development and is robust to missing data in the output measurements. The proposed prediction model is based on a latent variables model to extract glycemic dynamics from highly-correlated data and incorporates prior knowledge of exponential stability to improve the prediction ability. Missing data structures are formulated to enable model predictions when output measurements are missing for short periods of time. Based on the latent variables model, the MPC strategy and adaptive rules are developed to automatically tune the aggressiveness of the MPC. The adaptive prior-knowledge-informed MPC is evaluated with computer simulations for the control of blood glucose concentrations in people with Type 1 diabetes (T1D) using simulated virtual patients. Due to the variability among people with T1D, the hyperparameters of the prior-knowledge-informed model are personalized to individual subjects. The percentage of time spent in the target range is 76.48% when there are no missing data and 76.52% when there are missing data episodes lasting up to 30 mins (6 samples). Incorporating the adaptive rules further improves the percentage of time in target range to 84.58% and 84.88% for cases with no missing data and missing data, respectively. The proposed adaptive prior-informed MPC formulation provides robust, effective, and safe regulation of glucose concentration in T1D despite disturbances and missing measurements.
- Published
- 2023
8. Adaptive personalized multivariable artificial pancreas using plasma insulin estimates
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Sediqeh Samadi, Iman Hajizadeh, Nicole Hobbs, Mert Sevil, Rachel Brandt, Mudassir Rashid, and Ali Cinar
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0209 industrial biotechnology ,Computer science ,Multivariable calculus ,Glucose Measurement ,02 engineering and technology ,Artificial pancreas ,Industrial and Manufacturing Engineering ,Computer Science Applications ,Model predictive control ,020901 industrial engineering & automation ,020401 chemical engineering ,Control and Systems Engineering ,Control theory ,Modeling and Simulation ,0204 chemical engineering ,Plasma insulin ,Subspace topology ,Glycemic - Abstract
An adaptive and personalized multivariable artificial pancreas (mAP) system using plasma insulin estimates is proposed to efficiently accommodate major disturbances to the blood glucose concentration, such as meal and physical activity. Accurate adaptive glycemic models are developed through a recursive subspace identification technique with wearable physiological measurements and estimates of unannounced meal effect and plasma insulin concentration (PIC) along with continuous glucose concentration signals to characterize the glucose concentration dynamics under various conditions such as food consumption and physical activity. The identified models with time-varying parameters are employed in the design of an adaptive model predictive control (MPC) system that is cognizant of the PIC. The adaptive controller parameters, dynamic PIC constraint, addition of physiological measurements from wearable devices, feature variables generated from the glucose measurements, and estimation of uncertain model parameters, including the meal effect, enable the mAP system to effectively compute the optimal insulin infusion over diverse diurnal variations without meal and exercise announcements. Simulation case studies using a multivariable simulator demonstrate the efficacy of the proposed mAP system.
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- 2019
9. Ensuring Stability and Fidelity of Recursively Identified Control-Relevant Models
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Iman Hajizadeh, Ali Cinar, and Mudassir Rashid
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0209 industrial biotechnology ,Correctness ,Computer science ,media_common.quotation_subject ,Stability (learning theory) ,System identification ,Fidelity ,030209 endocrinology & metabolism ,02 engineering and technology ,03 medical and health sciences ,Noise ,Identification (information) ,020901 industrial engineering & automation ,0302 clinical medicine ,Control and Systems Engineering ,Heuristics ,Algorithm ,Subspace topology ,media_common - Abstract
In this work, a recursive system identification algorithm is extended to improve reliability and better handle stochastic disturbances, measurement noise, and other adverse phenomena. The proposed approach involves the modification of the recursive predictor-based subspace identification (PBSID) algorithm to incorporate constraints on the fidelity and accuracy of the identified models, correctness of the sign of the input-to-output gains, and the integration of heuristics to ensure stability of the recursively identified models. The efficacy of the proposed approach is demonstrated using case studies involving the modeling of time-varying glucose–insulin dynamics.
- Published
- 2018
10. Adaptive Model Predictive Control for Nonlinearity in Biomedical Applications
- Author
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Sediqeh Samadi, Ali Cinar, Mudassir Rashid, Mert Sevil, Rachel Brandt, Nicole Hobbs, and Iman Hajizadeh
- Subjects
0209 industrial biotechnology ,Optimization problem ,Computer science ,Feature extraction ,System identification ,02 engineering and technology ,Artificial pancreas ,Model predictive control ,Nonlinear system ,020901 industrial engineering & automation ,020401 chemical engineering ,Control and Systems Engineering ,Control theory ,Robustness (computer science) ,0204 chemical engineering ,Subspace topology - Abstract
An adaptive model predictive control (MPC) formulation is proposed in this work for optimal insulin dosing decisions in artificial pancreas (AP) systems. To this end, a recursive subspace-based system identification approach is used to characterize the transient dynamics of biological systems, specifically the metabolic processes involved in diabetes. Subsequent to system identification, an adaptive MPC algorithm is designed using the recursively identified models to effectively compute the optimal insulin delivery for AP systems. A feature extraction method based on glucose measurements is used to detect rapid deviations from the desired set-point caused by significant disturbances and subsequently modify the constraints of the optimization problem for negotiating between the aggressiveness and robustness of the controller to suggest the required amount of insulin. The efficacy of the proposed adaptive MPC is demonstrated using simulation case studies.
- Published
- 2018
11. Fault Detection in Continuous Glucose Monitoring Sensors for Artificial Pancreas Systems
- Author
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Nicole Hobbs, Zacharie Maloney, Jianyuan Feng, Ali Cinar, Mert Sevil, Caterina Lazaro, Mudassir Rashid, Sediqeh Samadi, Iman Hajizadeh, and Xia Yu
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Type 1 diabetes ,Glucose control ,Continuous glucose monitoring ,business.industry ,Computer science ,0206 medical engineering ,030209 endocrinology & metabolism ,Pattern recognition ,02 engineering and technology ,medicine.disease ,020601 biomedical engineering ,Artificial pancreas ,Fault detection and isolation ,03 medical and health sciences ,Nonlinear system ,0302 clinical medicine ,Control and Systems Engineering ,Kernel (statistics) ,medicine ,Artificial intelligence ,business ,Glycemic - Abstract
Continuous glucose monitoring (CGM) sensors are a critical component of artificial pancreas (AP) systems that enable individuals with type 1 diabetes to achieve tighter blood glucose control. CGM sensor signals are often afflicted by a variety of anomalies, such as biases, drifts, random noises, and pressure-induced sensor attenuations. To improve the accuracy of CGM measurements, an on-line fault detection method is proposed based on sparse recursive kernel filtering algorithms to identify faults in glucose concentration values. The fault detection algorithm is designed to effectively handle the nonlinearity of the measurements and to differentiate the normal variability in the glycemic dynamics from sensor anomalies. The effectiveness of the proposed recursive kernel filtering algorithm for sensor error detection is demonstrated using simulation studies.
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- 2018
12. Multivariable Recursive Subspace Identification with Application to Artificial Pancreas Systems
- Author
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Sediqeh Samadi, Jianyuan Feng, Kamuran Turksoy, Caterina Lazaro, Mudassir Rashid, Elizabeth Littlejohn, Nicole Frantz, Iman Hajizadeh, Mert Sevil, Ali Cinar, and Zacharie Maloney
- Subjects
0209 industrial biotechnology ,Engineering ,State-space representation ,Mean squared error ,business.industry ,Multivariable calculus ,System identification ,030209 endocrinology & metabolism ,Pattern recognition ,02 engineering and technology ,Variance (accounting) ,Artificial pancreas ,03 medical and health sciences ,Identification (information) ,020901 industrial engineering & automation ,0302 clinical medicine ,Control and Systems Engineering ,Control theory ,Artificial intelligence ,business ,Subspace topology - Abstract
Designing a fully automated artificial pancreas (AP) system is challenging. Changes in the glucose-insulin dynamics in the human body over time, and the inter-subject and day-to-day variability of people with type 1 diabetes (T1D) are two important factors that would highly undermine the performance of an AP that is based on time-invariant and non-individualized models. People with T1D show different responses to carbohydrate intake, insulin, physical activity and stress with day-to-day variability present between or within specific patients. Thus, the control law in an AP system requires a reliable time-varying individualized model to perform efficiently. In this work, a novel recursive identification approach called a Predictor-Based Subspace Identification (PBSID) method is used for identifying a linear time-varying glucose-insulin model for each individual. Model identification and validation are based on clinical data from closed-loop experiments. The models are evaluated by means of various performances indices: Variance Accounted For (VAF), Root mean square error (RMSE), Normalized root mean square error (NRMSE) and Normalized mean square error (NMSE). The proposed method provides a stable time-varying state space model over time. It can be also individualized for each patient by defining the order of the system correctly. The approach proposed in this work has shown a strong potential to identify a consistent glucose-insulin model in real time for use in an AP system.
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- 2017
13. Multi-rate modeling and economic model predictive control of the electric arc furnace
- Author
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Mudassir Rashid, Prashant Mhaskar, and Christopher L.E. Swartz
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0209 industrial biotechnology ,Engineering ,business.industry ,Final product ,Process (computing) ,Control engineering ,Scrap ,02 engineering and technology ,Industrial and Manufacturing Engineering ,Computer Science Applications ,Weighting ,Electric arc ,Nonlinear system ,Noise ,020901 industrial engineering & automation ,020401 chemical engineering ,Control and Systems Engineering ,Modeling and Simulation ,0204 chemical engineering ,business ,Electric arc furnace - Abstract
In this Chapter, we consider the problem of multi-rate modeling and economic model predictive control (EMPC) of electric arc furnaces (EAF), which are widely used in the steel industry to produce molten steel from scrap metal. The two main challenges that we address are the multi-rate nature of the measurement availability, and the requirement to achieve final product of a desired characteristic, while minimizing the operation cost. To this end, multi-rate models are identified that include predictions for both the infrequently and frequently measured process variables. The models comprise local linear models and an appropriate weighting scheme to capture the nonlinear nature of the EAF. The resulting model is integrated into a two-tiered predictive controller that enables achieving the target end-point while minimizing the associated cost. The EMPC is implemented on the EAF process and the closed-loop simulation results subject to the limited availability of process measurements and noise illustrate the improvement in economic performance over existing trajectory-tracking approaches.
- Published
- 2016
14. Simulation software for assessment of nonlinear and adaptive multivariable control algorithms: Glucose–insulin dynamics in Type 1 diabetes
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Nicole Hobbs, Ali Cinar, Zacharie Maloney, Rachel Brandt, Jianyuan Feng, Laurie Quinn, Mert Sevil, Sediqeh Samadi, Mudassir Rashid, Minsun Park, Iman Hajizadeh, and Paul Kolodziej
- Subjects
Type 1 diabetes ,Automatic control ,Computer science ,020209 energy ,General Chemical Engineering ,Insulin ,medicine.medical_treatment ,Multivariable calculus ,02 engineering and technology ,medicine.disease ,computer.software_genre ,Accelerometer ,Article ,Computer Science Applications ,Simulation software ,Nonlinear system ,020401 chemical engineering ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,0204 chemical engineering ,computer ,Glycemic - Abstract
A simulator for testing automatic control algorithms for nonlinear systems with time-varying parameters, variable time delays, and uncertainties is developed. It is based on simulation of virtual patients with Type 1 diabetes (T1D). Nonlinear models are developed to describe glucose concentration (GC) variations based on user-defined scenarios for meal consumption, insulin administration, and physical activity. They compute GC values and physiological variables, such as heart rate, skin temperature, accelerometer, and energy expenditure, that are indicative of physical activities affecting GC dynamics. This is the first simulator designed for assessment of multivariable controllers that consider supplemental physiological variables in addition to GC measurements to improve glycemic control. Virtual patients are generated from distributions of identified model parameters using clinical data. The simulator will enable testing and evaluation of new control algorithms proposed for automated insulin delivery as well as various control algorithms for nonlinear systems with uncertainties, time-varying parameters and delays.
- Published
- 2019
15. A non-Gaussian pattern matching based dynamic process monitoring approach and its application to cryogenic air separation process
- Author
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Junichi Mori, Honglu Yu, Gangshi Hu, Jie Yu, Lawrence Megan, Jingyan Chen, Jesus Flores-Cerrillo, and Mudassir Rashid
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Engineering ,business.industry ,General Chemical Engineering ,Gaussian ,Pattern recognition ,Mutual information ,Fault (power engineering) ,Independent component analysis ,Fault detection and isolation ,Computer Science Applications ,symbols.namesake ,Principal component analysis ,symbols ,Pattern matching ,Artificial intelligence ,business ,Subspace topology - Abstract
Principal component analysis (PCA) based pattern matching methods have been applied to process monitoring and fault detection. However, the conventional pattern matching approaches do not specifically take into account the non-Gaussian dynamic features in chemical processes. Furthermore, those techniques are more focused on fault detection instead of fault diagnosis. In this study, a non-Gaussian pattern matching based fault detection and diagnosis method is developed and applied to monitor cryogenic air separation process. First, independent component analysis (ICA) models are built on the normal benchmark and monitored data sets along sliding windows. The IC subspaces from the benchmark and monitored data are then extracted to evaluate the non-Gaussian patterns and detect process faults through a mutual information based dissimilarity index. Further, a difference subspace between the two IC subspaces is computed to characterize the divergence of the dynamic and non-Gaussian patterns between the benchmark and monitored data. Subsequently, the mutual information between the IC difference subspace and each process variable direction is defined as a new non-Gaussian contribution index for fault identification and diagnosis. The presented approach is applied to a simulated cryogenic air separation plant and the monitoring results are compared against those of PCA based pattern matching techniques and ICA based monitoring method. The application study demonstrates that the developed non-Gaussian pattern matching approach can effectively monitor the complex air separation process with superior fault detection and diagnosis capability.
- Published
- 2013
16. A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction
- Author
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Kuilin Chen, Jie Yu, Junichi Mori, and Mudassir Rashid
- Subjects
Engineering ,Wind power ,business.industry ,Mechanical Engineering ,Gaussian ,Posterior probability ,Building and Construction ,Bayesian inference ,Pollution ,Industrial and Manufacturing Engineering ,Wind speed ,symbols.namesake ,General Energy ,Kriging ,Moving average ,Physics::Space Physics ,Statistics ,symbols ,Autoregressive integrated moving average ,Electrical and Electronic Engineering ,business ,Algorithm ,Physics::Atmospheric and Oceanic Physics ,Civil and Structural Engineering - Abstract
Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction.
- Published
- 2013
17. A Bayesian model averaging based multi-kernel Gaussian process regression framework for nonlinear state estimation and quality prediction of multiphase batch processes with transient dynamics and uncertainty
- Author
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Kuilin Chen, Jie Yu, and Mudassir Rashid
- Subjects
Computer science ,Applied Mathematics ,General Chemical Engineering ,Posterior probability ,Process (computing) ,General Chemistry ,Bayesian inference ,Mixture model ,Industrial and Manufacturing Engineering ,Nonlinear system ,Kriging ,Kernel (statistics) ,Statistics ,Transient (oscillation) ,Algorithm - Abstract
Batch processes are characterized by inherent nonlinearity, multiplicity of operating phases, between-phase transient dynamics and batch-to-batch uncertainty that pose significant challenges for accurate state estimation and quality prediction. Conventional multi-model strategies, however, may be ill-suited for multiphase batch processes because the localized models do not specially take into account the complex transient dynamics between two consecutive operating phases. In this study, a novel Bayesian model averaging based multi-kernel Gaussian process regression (BMA-MKGPR) approach is proposed for state estimation and quality prediction of nonlinear batch processes with multiple operating phases and between-phase transient dynamics. A kernel mixture model strategy is first used to identify the different operating phases of batch processes and then the multi-kernel GPR models are built for all the identified phases. Further, the between-phase transitional stage is determined by the posterior probabilities of measurement samples with respect to the two consecutive phases so that the Bayesian model averaging strategy can be designed to incorporate the two localized GPR models for handling the between-phase transient dynamics. For an arbitrary test sample within the transitional stage, its posterior probabilities with respect to the local models corresponding to the two consecutive phases are set as the adaptive weightings to integrate the corresponding local GPR models for state estimation and quality prediction. The proposed BMA-MKGPR approach is applied to a multiphase batch polymerization process and the result comparison demonstrates that the presented method can effectively handle multiple nonlinear operating phases, between-phase transient dynamics and process uncertainty with fairly high prediction accuracies.
- Published
- 2013
18. A new dissimilarity method integrating multidimensional mutual information and independent component analysis for non-Gaussian dynamic process monitoring
- Author
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Jie Yu and Mudassir Rashid
- Subjects
business.industry ,Process Chemistry and Technology ,Gaussian ,Multivariate normal distribution ,Pattern recognition ,Mutual information ,Independent component analysis ,Computer Science Applications ,Analytical Chemistry ,Index of dissimilarity ,symbols.namesake ,ComputingMethodologies_PATTERNRECOGNITION ,Component (UML) ,Principal component analysis ,Partial least squares regression ,symbols ,Artificial intelligence ,business ,Spectroscopy ,Software ,Mathematics - Abstract
Traditional multivariate statistical processes monitoring (MSPM) techniques like principal component analysis (PCA) and partial least squares (PLS) are not well-suited in monitoring non-Gaussian processes because the derivation of T 2 and SPE indices requires the approximate multivariate Gaussian distribution of the process data. In this paper, a novel pattern analysis driven dissimilarity approach is developed by integrating multidimensional mutual information (MMI) with independent component analysis (ICA) in order to quantitatively evaluate the statistical dependency between the independent component subspaces of the normal benchmark and monitored data sets. The new MMI based ICA dissimilarity index is derived from the higher-order statistics so that the non-Gaussian process features can be extracted efficiently. Moreover, the moving-window strategy is used to deal with process dynamics. The multidimensional mutual information based ICA dissimilarity method is applied to the Tennessee Eastman Chemical process. The process monitoring results of the proposed method are demonstrated to be superior to those of the regular PCA, PCA dissimilarity, regular ICA and angle based ICA dissimilarity approaches.
- Published
- 2012
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