Many design problems, including control design problems, involve infinite dimensional constraints of the form ϕ(z, α) ≤ 0 for all α ε A where α denotes time or frequency or a parameter vector. In other design pro...
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Many design problems, including control design problems, involve infinite dimensional constraints of the form ϕ(z, α) ≤ 0 for all α ε A where α denotes time or frequency or a parameter vector. In other design problems, tuning or trimming of certain parameters, after manufacture of the system, is permitted; the corresponding constraint is that for each α in A there exists a value τ (of the tuning parameter) in a permissible set T such that φ(z,α,t) < 0. New algorithms for solving design problems having such constraints are described.
We report on some recent results, in which it is shown that the infinite time quadratic cost control problem for linear systems with state and control delays can be solved using evolution equation techniques. The basi...
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We report on some recent results, in which it is shown that the infinite time quadratic cost control problem for linear systems with state and control delays can be solved using evolution equation techniques. The basis of our approach is a delay free evolution equation with bounded input operator governing the state, which permits application of the abstract theory of quadratic cost control of linear systems defined on Hilbert spaces.
This paper collects a number of scattered results on the solution of both finite and infinite systems of inequalities into a unified whole. In particular, it presents two methods which solve finite systems of inequali...
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This paper collects a number of scattered results on the solution of both finite and infinite systems of inequalities into a unified whole. In particular, it presents two methods which solve finite systems of inequalities in a finite number of iterations and which use outer approximations techniques for infinite systems of inequalities.
The problem of estimating the state variables from measurements in an electric-power system is considered. The conventional linearised least-squares solution is shown to be ineffective in the presence of gross measure...
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The problem of estimating the state variables from measurements in an electric-power system is considered. The conventional linearised least-squares solution is shown to be ineffective in the presence of gross measurement errors. Reformulating the problem as a linear program leads to a state estimator that combines the advantages of noise filtering and bad-data elimination, and may be implemented straightforwardly by application of the simplex method. The solution of various examples based on three test networks confirms the advantages of the method especially where the data are corrupted by a number of gross errors. Depending on the degree of redundancy in the measurement set, the computational requirements of the method are comparable with conventional least-squares solution. For real-time power-system monitoring and control where process variables have unknown statistics, the linear-programming method is believed to be more efficient than conventional algorithms.
A stable proper right (left) n th-order inverse of a given linear time-invariant system of order n can always be constructed, via a simple algorithm, if a proper right (left) inverse exists and the zeros of the given ...
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A stable proper right (left) n th-order inverse of a given linear time-invariant system of order n can always be constructed, via a simple algorithm, if a proper right (left) inverse exists and the zeros of the given system are stable. Furthermore, it is shown that all of the poles of this inverse can be arbitrarily assigned except those which equal the zeros of the given system.
The above paper has presented a number of methods for designing "optimal" inputs for system identification based on maximization of the trace of the information matrix. This approach leads to simple design a...
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The above paper has presented a number of methods for designing "optimal" inputs for system identification based on maximization of the trace of the information matrix. This approach leads to simple design algorithms. However caution must be exercised in interpreting the results as it will be shown in this note that the resulting inputs can, in many instances, give a singular information matrix, i.e, ambiguity in parameter estimates.
This note gives the necessary conditions for local identifiability of innovations models of linear dynamic systems. It is shown that the information matrix is singular if the input power density spectrum contains less...
It is shown that the estimate of transient behavior of composite systems presented in the previous paper can be extended to the case where some of the subsystems are not stable.
It is shown that the estimate of transient behavior of composite systems presented in the previous paper can be extended to the case where some of the subsystems are not stable.
Hyperspectral (HS) imaging is a valuable technique for accurately classifying materials because of the abundance of spectral information and high resolution it provides. However, the characteristics of Hyperspectral i...
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Hyperspectral (HS) imaging is a valuable technique for accurately classifying materials because of the abundance of spectral information and high resolution it provides. However, the characteristics of Hyperspectral images (HSI), such as high-dimensional features and information redundancy, pose significant challenges to data processing. Traditional dimensionality reduction methods often have information loss, high computational complexity, and easy to ignore the strong correlation between HSI spectral bands when dealing with HSI data. Although other methods can achieve satisfactory classification performance, they do not consider the dimensionality reduction of HSI, and they focus on the model performance, which limits further improvement in classification performance. This paper proposes a transformer-based framework called “SpectrumRecombineFormer” (SRF), which is composed of two key modules, namely “Spatial Spectral ReCombination” (SSRC) and “Cross-layer Fusion” (CF). The SSRC is capable of utilizing both adjacent and non-adjacent spectrums to generate the spatial-sequential perceptive representations, which alleviates the effect of the strong correlation between HSI spectral bands. The CF can avoid the loss of information during the feed-forward procedure among layers. Extensive experiments on five existing datasets (widely-adopted Indian Pines, Houston2013, Pavia University, Salinas and KSC) demonstrate the capability of our proposed method to address the above mentioned challenges. Both quantitative and qualitative experimental ablation studies, including visualization results, reveal that the proposed SRF method can successfully and efficiently classify hyperspectral images and surpass the other state-of-the-art methods. For access to the source code, please visit https://***/kangpeilun/SRF-HSI-Classification-master.
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