Model predictive controller presented in this article makes use of both weather forecast and thermal model of a building to control inside temperature. this, by sharp contrast to conventional control strategies such a...
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Model predictive controller presented in this article makes use of both weather forecast and thermal model of a building to control inside temperature. this, by sharp contrast to conventional control strategies such as weather-compensated heating control (heating-curve controller), enables utilization of thermal capacity of the building, thus minimization of energy consumption. the inside temperature can be maintained at desired levels independent of the outside weather conditions using modified formulation of predictive controller. Nevertheless, proper identification of the building model is crucial. the models of multiple-input multiple-output systems can be identified using subspace methods. the controller was tested on (and applied to) the real building and results were compared with a present heating control.
In this paper we present singular and singularly impulsive model of genetic regulatory networks. Concrete example consists of two gene - two proteins simple synthetic network, and posses two fundamentally present part...
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In this paper we present singular and singularly impulsive model of genetic regulatory networks. Concrete example consists of two gene - two proteins simple synthetic network, and posses two fundamentally present parts in biochemical networks - positive and negative feedback loops. these are important structural parts of biochemical networks and are interesting for control theoreticians. By investigating purpose of positive and negative feedback presence, we see for example that they lead either to bistability (or multistability, in general) in case of positive feedback or generate oscillatory behaviour in case of negative feedback what we show. Mathematical model is derived in form of nonlinear two dimensional dynamical system which is further approximated to singular and singularly impulsive dynamical system. Importance of this example is singular systems approximation which in this particular example leads to interesting and well known phenomena - of relaxation oscillation. this phenomena is consequence of multiple-scale network, i.e. fast-slow decomposition. Jump phenomena that appears as a consequence of time scale differences is very different from jumps (impulsive behaviour) that we have in impulsive systems approximation of, for example, sigmoidal function response by piece-wise linear function in order to reduce complexity.
this study aims at developing abstract metamodels for approximating highly nonlinear relationships within a metal casting plant. Metal casting product quality nonlinearly depends on many controllable and uncontrollabl...
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ISBN:
(纸本)9781424478149
this study aims at developing abstract metamodels for approximating highly nonlinear relationships within a metal casting plant. Metal casting product quality nonlinearly depends on many controllable and uncontrollable factors. For improving the productivity of the system, it is vital for operation planners to predict in advance the amount of high quality products. Neural networks metamodels are developed and applied in this study for predicting the amount of saleable products. Training of meta-models is done using the Levenberg-Marquardt and Bayesian learning methods. Statistical measures are calculated for the developed metamodels over a grid of neural network structures. Demonstrated results indicate that Bayesian-based neural network metamodels outperform the Levenberg-Marquardt-based metamodels in terms of both prediction accuracy and robustness to the metamodel complexity. In contrast, the latter metamodels are computationally less expensive and generate the results more quickly.
In most shape optimization problems, the optimal solution does not belong to the set of genuine shapes but is a composite structure. the homogenization method consists in relaxing the original problem thereby extendin...
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In most shape optimization problems, the optimal solution does not belong to the set of genuine shapes but is a composite structure. the homogenization method consists in relaxing the original problem thereby extending the set of admissible structures to composite shapes. From the numerical viewpoint, an important asset of the homogenization method with respect to traditional geometrical optimization is that the computed optimal shape is quite independent from the initial guess (at least for the compliance minimization problem). Nevertheless, the optimal shape being a composite, a post-treatement is needed in order to produce an almost optimal non-composite (i.e. workable) shape. the classical approach consists in penalizing the intermediate densities of material, but the obtained result deeply depends on the underlying mesh used and the level of details is not controllable. In a previous work, we proposed a new post-treatement method for the compliance minimization problem of an elastic structure. the main idea is to approximate the optimal composite shape with a locally periodic composite and to build a sequence of genuine shapes converging toward this composite structure. this method allows us to balance the level of details of the final shape and its optimality. Nevertheless, it was restricted to particular optimal shapes, depending on the topological structure of the lattice describing the arrangement of the holes of the composite. In this article, we lift this restriction in order to extend our method to any optimal composite structure for the compliance minimization problem.
In time critical applications, anytime mode of operation offers a way to ensure continuous operation and to cope withthe possibly dynamically changing time and resource availability. Soft Computing, especially fuzzy ...
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ISBN:
(纸本)9789898425027
In time critical applications, anytime mode of operation offers a way to ensure continuous operation and to cope withthe possibly dynamically changing time and resource availability. Soft Computing, especially fuzzy model based operation proved to be very advantageous in power plant control where the high complexity, nonlinearity, and possible partial knowledge usually limit the usability of classical methods. Higher Order Singular Value Decomposition based complexity reduction makes possible to convert different classes of fuzzy models into anytime models, thus offering a way to combine the advantages, like low complexity, flexibility, and robustness of fuzzy and anytime techniques. By this, a model based anytime control methodology can be suggested which is able to keep on continuous operation using non-exact, approximate models of the plant, thus preventing critical breakdowns in the operation. In this paper, an anytime modeling method is suggested which makes possible to use complexity optimized fuzzy models in control. the technique is able to filter out the redundancy of fuzzy models and can determine the near optimal non-exact model of the plant considering the available time and resources. It also offers a way to improve the granularity (quality) of the model by building in new information without complexity explosion.
Many age estimation methods have been proposed for various applications such as Age Specific Human Computer Interaction (ASHCI) system, age simulation system and so on. Because the performance of the age estimation is...
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ISBN:
(纸本)9781424478149
Many age estimation methods have been proposed for various applications such as Age Specific Human Computer Interaction (ASHCI) system, age simulation system and so on. Because the performance of the age estimation is greatly affected by the aging feature, the aging feature extraction from facial images is very important. the aging features used in previous works can be divided into global and local features. As global features, Active Appearance models (AAM) was mainly used for age estimation in previous works. However, AAM is not enough to represent local features such as wrinkle and skin. therefore, the research about local features is required. In previous works, local features were generally used to determine age group rather than detailed age, and the comparative studies about various local features extraction methods were not conducted. In this paper, the performances of sobel filter, difference image between original and smoothed image, ideal high pass filter (IHPF), gaussian high pass filter (GHPF), Haar and Daubechies discrete wavelet transform (DWT) are compared for extracting local features and detailed age estimation is performed by Support Vector Regression (SVR) on BERC and PAL aging database. the experimental results show that local features can be used for detailed age estimation and GHPF gives a better performance than other methods.
According to the level of information provided in images, segmentation techniques can be categorized into two groups. One is region-labeling, which obeys the intensity-based classification methods. Although modeling t...
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ISBN:
(纸本)9781424478149
According to the level of information provided in images, segmentation techniques can be categorized into two groups. One is region-labeling, which obeys the intensity-based classification methods. Although modeling the tissue intensity is straightforward by applying local statistical methods and spatial dependencies, the results might suffer from noise and incomplete data. the second group of techniques applies active contour models, in which the objective is to find the optimal partition of the image domain using a closed or open curve by using prior constraints on the shape variation. However, estimating optimal curve is intractable due to the incomplete observation data. this paper extends a previously reported joint active contour model for medical image segmentation in a new Expectation-Maximization (EM) framework, wherein the evolution curve is constrained not only by a shape-based statistical model but also by applying a hidden variable model from the image observation. In this approach, the hidden variable model is defined by the local voxel labeling computed from its likelihood function, depended on the image functions and the prior anatomical knowledge. Comparative results on segmenting putamen and caudate shapes in MR brain images confirmed both robustness and accuracy of the proposed curve evolution algorithm.
Path-tracking by using MPC (model predictive control) is a suitable control science solution for mobile robot navigation applications. Online MPC is reported by using short-term horizons that allow dealing with flexib...
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Path-tracking by using MPC (model predictive control) is a suitable control science solution for mobile robot navigation applications. Online MPC is reported by using short-term horizons that allow dealing with flexible path-tracking and reactive behaviors. the majority of MPC experimental research developed is based on the fact that the reference trajectory is known beforehand. However, under dynamic environments the global solution becomes unfeasible for the majority of applications where the scenario should be considered as partially unknown due to the lack of global sensors or the existence of dynamic obstacles. Moreover, traditional motion control of wheeled mobile robots (WMRs) is achieved by using discontinuous control laws that are implemented through low level velocity PID controllers. Instead of using such methods, this work proposes to use local MPC as a useful methodology for WMR navigation under dynamic environments or as obstacle avoidance strategy. In this way, the desirable path coordinates are used in the control law as a way for obtaining the robot speed commands. Simulation results are used for addressing online MPC implementations. the system is on-robot tested by using simple on board perception systems. In this context, a local occupancy grid is built by using dead-reckoning and monocular data.
this article presents a novel learning methodology based on the hybrid mechanism for training an interval type-2 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems (FLS). Using input-output data pairs during ...
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this article presents a novel learning methodology based on the hybrid mechanism for training an interval type-2 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems (FLS). Using input-output data pairs during the forward pass of the training and prediction processes, the interval type-2 non-singleton type-2 TSK FLS the consequent parameters were tuned by using the recursive least squares (RLS) method. In the backward pass, the antecedent parameters were tuned by using the back-propagation (BP) method. As reported in the literature, the performance indexes of these hybrid models have proved to be better than the individual training mechanism when used alone. the proposed hybrid methodology was tested thru the modeling and prediction of the steel strip temperature at the descaler box entry as rolled in an industrial hot strip mill. Results show that the proposed method compensates better for uncertain measurements than previous type-2 Takagi-Sugeno-Kang using non-hybrid or only back propagation learning mechanisms.
Navigation systems used in recent days rely mainly on Kalman filter to fuse data from global positioning system (GPS) and the inertial navigation system (INS). In common, INS/GPS data fusion provides reliable navigati...
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Navigation systems used in recent days rely mainly on Kalman filter to fuse data from global positioning system (GPS) and the inertial navigation system (INS). In common, INS/GPS data fusion provides reliable navigation solution by overcoming drawbacks such as signal blockage for GPS and increase in position errors with time for INS. Kalman filtering INS/GPS integration techniques used in present days have some inadequacies related to the stochastic error models of inertial sensors, immunity to noise, and observability. this paper aims to introduce a new system integration approach for fusing data from INS and GPS utilizing artificial neural networks (ANN). A multi-layer perceptron ANN has been recently suggested to fuse data from INS and differential GPS (DGPS). though the integrated system using multi-layer perceptron scheme improves the positioning accuracy, it has shortcomings like complexity with respect to the architecture of multi-layer perceptron networks and limitation of online training algorithm to provide real-time capabilities. this paper, therefore, proposes the use of an alternative ANN architecture. the proposed architecture is based on radial basis function (RBF) neural networks, which generally have simpler architecture and faster training procedures than multi-layer perceptron networks. the RBF-ANN module is trained to predict the INS position error and provide accurate positioning of the moving vehicle.
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