Digital watermarking has been researched extensively due to its potential use for data security and copyright protection. Much of the literature has focused on developing invisible watermarking algorithms. However, no...
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This paper proposes the software package SISCON, dedicated to the evaluation of optimal decisions for large scale systems. SISCON firstly evaluates mathematical models developed from experimental data using LS methods...
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This paper proposes the software package SISCON, dedicated to the evaluation of optimal decisions for large scale systems. SISCON firstly evaluates mathematical models developed from experimental data using LS methods for linear and nonlinear systems and after that computes the optimal decision problems, solving the mathematical non-linear programming problems. The large scale systems have generally a complex structure and global approach computation cannot be carried out. The authors present a decentralised decision structure having a well-defined distribution of supervisory functions. After decomposition of large – scale problems is carried out, sub problems are solved using standard optimization techniques. SISCON offers opportunities for solving non-linear mathematical programming problems and for evaluating optimal decisions in large scale systems control.
The paper presents the requirements of measurement system for electric arc furnace parameters measurement and for working characteristic calculating. On this basis measurement signals were specified and measurement al...
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The paper presents the requirements of measurement system for electric arc furnace parameters measurement and for working characteristic calculating. On this basis measurement signals were specified and measurement algorithms were developed. For data acquisition a special system was constructed. The system consists of portable computer with acquisition card and conditioning system with differential amplifiers, antialiasing filters and sample and hold circuits. The system is connected with interface build-up permanent in arc furnace. The system software makes possible on-line observation of measured signals values and save data to hard disk. Further calculations may be performed off-line using special developed algorithms. For system software development the open source Python language and GCC compiler for Windows were used.
The main objective of this paper is to show how one can benefit from using Iterative Learning control instead of conventional feedback control. As a main result it is shown that even if the nominal plant satisfies a g...
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This paper revisits the Arimoto-algorithm in the discrete-time case. It is shown that if a plant satisfies a positivity condition, there always exists a learning gain so that the algorithm converges monotonically to z...
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This tutorial describes experiences with applying a control theoretical approach to achieving performance guarantees in Web servers, with emphasis on delay control. A model for the server is formulated and translated ...
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This tutorial describes experiences with applying a control theoretical approach to achieving performance guarantees in Web servers, with emphasis on delay control. A model for the server is formulated and translated into a control problem formulation. Limitations of the control theoretic approach is identified that arise due to system non-linearities and modeling inaccuracies. Solutions are proposed that augment the feedback control framework with elements of scheduling and queueing theory. The theoretical results and QoS control loops presented by the authors are implemented in a middleware package, called controlWare, which provides the software mechanisms and interfaces that allow control of real server performance. Implementation and performance of controlWare is described.
control methods are being used increasingly for uncertainty management and QoS in modern Web server systems. Previous approaches have suggested combined feedforward and feedback control strategies, using queuing theor...
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control methods are being used increasingly for uncertainty management and QoS in modern Web server systems. Previous approaches have suggested combined feedforward and feedback control strategies, using queuing theory for feedforward delay prediction. While queuing theory allows one to predict delay as a function of arrival and service rates, the prediction applies only to long-term averages, and is therefore insensitive to sudden load changes. Unfortunately, Internet load is very bursty, leaving room for predictor improvement. The main contribution of this paper is an extension of the combined feedforward/feedback framework in which the queuing model is replaced with a predictor that instead uses instantaneous measurements to predict future delays. The proposed strategy is evaluated in simulation and by experiments on an Apache Web server. It is shown that the approach performs better than the combined queuing model based feedforward and feedback control presented in earlier papers.
This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obt...
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This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obtained dynamic model, an integrated predictive control algorithm is proposed to position precisely with either stationary or moving obstacle avoidance. This objective is achieved automatically by introducing distant constraints into the open-loop optimization of control inputs. Simulation results demonstrate the feasibility of such control strategy for the deduced dynamic model
Document clustering is one of the popular techniques that assist users in organizing collections of documents. Two successful models of unsupervised neural networks, self-organizing map (SOM) and adaptive resonance th...
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Document clustering is one of the popular techniques that assist users in organizing collections of documents. Two successful models of unsupervised neural networks, self-organizing map (SOM) and adaptive resonance theory (ART), have shown promising results in this task. Most of the existing neural network based document clustering techniques rely on a "bag of words" document representation. Each word in the document is considered as a separate feature, ignoring the word order. We investigate the use of phrases rather than words as document features applied to our proposed document clustering technique, called hierarchical SOMART (HSOMART), which is a hierarchical network built up from independent SOM and ART neural networks. We describe a phrase grammar extraction technique, and the proposed HSOMART. The experimental results of clustering documents from the REUTERS corpus using the extracted phrases as features show an improvement in the clustering performance evaluated using the entropy and F-measure.
A novel local receding horizon control with contractive constraints is proposed in this paper. The main feature of the proposed control algorithm is to form open-loop optimizations in MPC locally. Thus the length of c...
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A novel local receding horizon control with contractive constraints is proposed in this paper. The main feature of the proposed control algorithm is to form open-loop optimizations in MPC locally. Thus the length of control horizons can be set relatively small, which is favorable for real-time applications. Moreover, the total and contractive coefficients in the proposed algorithm correspond to the prediction and control horizons in traditional MPC and they can be adjusted accordingly with clearer physical insights.
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