Linear programming-based solution methods for constrained partially observable Markov decision processes

Constrained partially observable Markov decision processes (CPOMDPs) have been used to model various real-world phenomena. However, they are notoriously difficult to solve to optimality, and there exist only a few approximation methods for obtaining high-quality solutions. In this study, grid-based approximations are used in combination with linear programming (LP) models to generate approximate policies for CPOMDPs. A detailed numerical study is conducted with six CPOMDP problem instances considering both their finite and infinite horizon formulations. The quality of approximation algorithms for solving unconstrained POMDP problems is established through a comparative analysis with exact solution methods. Then, the performance of the LP-based CPOMDP solution approaches for varying budget levels is evaluated. Finally, the flexibility of LP-based approaches is demonstrated by applying deterministic policy constraints, and a detailed investigation into their impact on rewards and CPU run time is provided. For most of the finite horizon problems, deterministic policy constraints are found to have little impact on expected reward, but they introduce a significant increase to CPU run time. For infinite horizon problems, the reverse is observed: deterministic policies tend to yield lower expected total rewards than their stochastic counterparts, but the impact of deterministic constraints on CPU run time is negligible in this case. Overall, these results demonstrate that LP models can effectively generate approximate policies for both finite and infinite horizon problems while providing the flexibility to incorporate various additional constraints into the underlying model.Comment: 42 pages, 8 figure

Development of a Data-Based Method for Performance Monitoring of Heat Exchangers

A multivariate analysis method is developed for processing measurements, and for detecting and isolating faults and monitoring performance degradation in heat exchanger control loops. A heat exchanger inside a typical temperature to flow cascade loop is considered. This system includes a constant speed pump with flow control valves, pressure and temperature measurement. A proportional-integral-differential (PID) controller is used to maintain a temperature set point for the exit flow on one side of the exchanger. A thermal-fluid model for the components in the system is developed. A Fault Detection and Isolation (FDI) rule-base is formulated from results of simulations performed using these models. Measurements from an installed laboratory heat exchanger control loop are also used. Faults simulated and induced on the physical heat exchanger loop include tube fouling, sensor drift, fluid leakage, unresponsive valves, plugged process lines, and controller errors. The rule base allows the identification of faults in a heat exchanger control loop given suitable process measurements

Cerebellar Asymmetry and Cortical Connectivity in Monozygotic Twins with Discordant Handedness

Handedness differentiates patterns of neural asymmetry and interhemispheric connectivity in cortical systems that underpin manual and language functions. Contemporary models of cerebellar function incorporate complex motor behaviour and higher-order cognition, expanding upon earlier, traditional associations between the cerebellum and motor control. Structural MRI defined cerebellar volume asymmetries and correlations with corpus callosum (CC) size were compared in 19 pairs of adult female monozygotic twins strongly discordant for handedness (MZHd). Volume and asymmetry of cerebellar lobules were obtained using automated parcellation.CC area and regional widths were obtained from midsagittal planimetric measurements. Within the cerebellum and CC, neurofunctional distinctions were drawn between motor and higher-order cognitive systems. Relationships amongst regional cerebellar asymmetry and cortical connectivity (as indicated by CC widths) were investigated. Interactions between hemisphere and handedness in the anterior cerebellum were due to a larger right-greater-than-left hemispheric asymmetry in right-handed (RH) compared to left-handed (LH) twins. In LH twins only, anterior cerebellar lobule volumes (IV, V) for motor control were associated with CC size, particularly in callosal regions associated with motor cortex connectivity. Superior posterior cerebellar lobule volumes (VI, Crus I, Crus II, VIIb) showed no correlation with CC size in either handedness group. These novel results reflected distinct patterns of cerebellar-cortical relationships delineated by specific CC regions and an anterior-posterior cerebellar topographical mapping. Hence, anterior cerebellar asymmetry may contribute to the greater degree of bilateral cortical organisation of frontal motor function in LH individuals

Printable all-dielectric water-based absorber

Abstract The phase interplay between overlapping electric and magnetic dipoles of equal amplitude generated by exclusively alldielectric structures presents an intriguing paradigm in the manipulation of electromagnetic energy. Here, we offer a holistic implementation by proposing an additive manufacturing route and associated design principles that enable the programming and fabrication of synthetic multi-material microstructures. In turn, we compose, manufacture and experimentally validate the first demonstrable 3d printed all-dielectric electromagnetic broadband absorbers that point the way to circumventing the technical limitations of conventional metal-dielectric absorber configurations. One of the key innovations is to judicially distribute a dispersive soft matter with a high-dielectric constant, such as water, in a low-dielectric matrix to enhance wave absorption at a reduced length scale. In part, these results extend the promise of additive manufacturing and illustrate the power of topology optimisation to create carefully crafted magnetic and electric responses that are sure to find new applications across the electromagnetic spectrum

Turkey's electricity consumption

A first order vector autoregression topology was used to model and predict Turkey's net electricity consumption in the future. Input variables for the model were the annual values of electricity consumption along with four demographic and economic indicators such as, population, gross domestic product, imports and exports. Output variables were the one-step-ahead values of the same variables. First, polynomial regressions were used to determine and remove the trend components of all these five variables. Then, principal components regression methodwas applied to evaluate the coefficients of the vector autoregression model. Electricity consumption of Turkey was modeled using annual data from 1970 to 2016 and the model was used to predict future consumption values until year 2030. Singular value decomposition was used to determine the number of important dimensions in the data. This approach yielded a significant reduction in the dimensionality of the problem and thus provided robustness to the predictions. The results showed the feasibility of applying principal components regression method to vector autoregression model for electricity consumption prediction.C1 [Kavaklioglu, Kadir] Pamukkale Univ, Mech Engn Dept, Denizli, Turkey

towards efficient residential building design

Partial least squares method was used to model residential building heating and cooling loads. These loads were modeled as functions of eight input variables such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution. The data for the models were taken from the literature and they consisted of values obtained through a commercial software package. Model validation was performed using k-fold cross validation method. Model validation was also performed using an analysis of total sum of squares of the data explained by the partial least squares latent variables. Validated models were compared against ordinary least squares models for heating and cooling loads, respectively. These models were used to determine the most influential input variables so that efficient building designs can be made. The results indicated that it is feasible to apply partial least squares regression to heating and cooling loads; and significant reduction in dimensionality may be achieved using the importance information provided by this method

Support Vector Regression

Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, epsilon-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values: and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best epsilon-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. (C) 2010 Elsevier Ltd. All rights reserved

Machines

This paper proposes artificial neural network and fuzzy system-based extreme learning machines (ELM) for offline and online modeling of U-tube steam generators (UTSG). Water level of UTSG systems is predicted in a one-step-ahead fashion using nonlinear autoregressive with exogenous input (NARX) topology. Modeling data are generated using a well-known and widely accepted dynamic model reported in the literature. Model performances are analyzed with different number of neurons for the neural network and with different number of rules for the fuzzy system. UTSG models are built at different reactor power levels as well as full range that corresponds to all reactor operating powers. A quantitative comparison of the models are made using the root-mean-squared error (RMSE) and the minimum-descriptive-length (MDL) criteria. Furthermore, conventional back propagation learning-based neural and fuzzy models are also designed for comparing ELMs to classical artificial models. The advantages and disadvantages of the designed models are discussed

direct evaporative cooling systems

Radial basis function network method is used for modeling a direct evaporative cooling system. Air dry exit temperature, air pressure drop across the cooler and cooler efficiency are predicted using these models. The inputs are pad thickness, air inlet speed, air dry inlet temperature, relative humidity at the inlet and feed water temperature. The data for the models are taken from the experiments performed specifically for this purpose. Model validation is performed using twofold cross validation method. A grid search is used to determine optimal network parameters, such as, optimum number of radial basis elements and spread parameter. Validated models are tested against ordinary least squares models for the output variables. The results indicate that it is feasible to apply radial basis function networks to model direct evaporative coolers. (C) 2018 Elsevier Ltd. All rights reserved

Artificial Neural Networks

Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (C) 2009 Elsevier Ltd. All rights reserved