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The integration of a European Electricity Market has leaded to one single market with multi-area which are considered as bidding zones; currently, it regards only the day-ahead market. The Price Coupling of Regions (PCR) project is an initiative for the day ahead market of seven power exchanges APX, Belpex, EPEX SPOT, GME, Nord Pool Spot, OMIE, and OTE. The Italian Power Exchange (GME) has become...
This paper introduces a new approach for the optimal management of reactive power, with emphasis on offshore wind power plants. The approach follows a predictive optimization scheme (i.e. day-ahead, intraday application). Predictive optimization is based on the principle of minimizing the real power losses, as well the number of On-load Tap Changer (OLTC) operations for daily time horizon (discretized...
In this paper, a unified differential evolution algorithm, named UDE, is presented for real parameter constrained optimization problems. The proposed UDE algorithm is inspired from some popular DE variants existing in the literature such as CoDE, JADE, SaDE, and ranking-based mutation operator. The primary feature of UDE lies in unifying the main idea of CoDE, JADE, SaDE, and ranking-based mutation...
In this paper, we consider iterative learning control(ILC) for discrete-time multi-agent system formation with one-step random time-delay. Random delays during transmission seriously affect the convergence performance of multi-agent formation. Based on one-step random time-delay model, the transition matrix of system is derived, which contains the impact factors of random delays. A learning control...
In the article the authors state and prove necessary and sufficient conditions for primitive one-variable functions to exist and the ways to find these functions.
The method of exact relaxation (ER) makes it possible to accelerate and stabilize the convergence of single-point iterative methods, but in multidimensional cases there exists “sphere of idling”, if hit by a value delivered by the basic algorithm, ER leaves the value unchanged despite computational expenses have already been done. From the other hand, a volume of the sphere is null. Therefore, deterministic...
Iris recognition is very difficult to perform as it requires an environment that is highly controlled for better image acquisition. As compared to other biometric technologies, iris recognition is prone to poor image quality. Specially, images captured from a distance introduce noises such as blur, off axis, specular reflections and occlusions. For proper recognition good quality of captured image...
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
One of auxiliary processes to obtain the optimal solution of an inverse problem is to use guaranteeing convergence strategy. Line-search strategy is the oldest and the most widely used one. However, line-search strategy is not suitable in some situations, in particular, in nonlinear situations where many local minima are present. Seeking an optimal solution for head tissue conductivities estimation...
Statistically-optimal Linear Discriminant Analysis (LDA) is formulated as a maximization that involves the nominal statistics of the classes to be discriminated. In practice, however, these nominal statistics are unknown and estimated from a collection of labeled training data. Accordingly, the nominal LDA basis is approximated by the solution of the popular practical LDA problem defined upon these...
Unknown global permutation of the separated sources, time-varying source activity and under determination are common problems affecting on-line Independent Vector Analysis when applied to real-world speech enhancement. In this work we propose to extend the signal model of IVA by introducing additional supervising components. Pilot signals, which are dependent on the sources, are injected in the multidimensional...
We present an incremental Broyden-Fletcher-Goldfarb-Shanno (BFGS) method as a quasi-Newton algorithm with a cyclically iterative update scheme for solving large-scale optimization problems. The proposed incremental quasi-Newton (IQN) algorithm reduces computational cost relative to traditional quasi-Newton methods by restricting the update to a single function per iteration and relative to incremental...
In this paper we analyze the convergence behavior of a sampling based system approximation process, where the time variable is in the argument of the signal and not in the argument of the bandlimited impulse response. We consider the Paley-Wiener space PWπ2 of bandlimited signals with finite energy and stable linear time-invariant (LTI) systems, and show that there are signals and systems such that...
It is known that there exist signals in Paley-Wiener space PWπ1 of bandlimited signals with absolutely integrable Fourier transform, for which the peak value of the Shannon sampling series diverges unboundedly. In this paper we analyze the structure of the set of signals which lead to strong divergence. Strong divergence is closely linked to the existence of adaptive methods. We prove that there exists...
This paper considers distributed multi-agents optimization problems where agents collaborate to minimize the sum of locally known convex functions. We focus on the case when the communication between agents is described by a directed graph. The proposed algorithm achieves the best known rate of convergence for this class of problems, O(μk) for 0 < μ < 1, given that the objective functions are...
The multiplicative update (MU) algorithm has been used extensively to estimate the basis and coefficient matrices in nonnegative matrix factorization (NMF) problems under a wide range of divergences and regularizations. However, theoretical convergence guarantees have only been derived for a few special divergences. In this work, we provide a conceptually simple, self-contained, and unified proof...
The paper presented a systematic evaluation of the weight sparsity regularization schemes for the deep neural networks applied to the whole brain resting-state functional magnetic resonance imaging data. The weight sparsity regularization was deployed between the visible and hidden layers of the Gaussian-Bernoulli restricted Boltzmann machine (GB-RBM), in which the L0-norm based non-zero value ratio...
This paper considers the problem of minimizing the average of a finite set of strongly convex functions. We introduce a double incremental aggregated gradient method (DIAG) that computes the gradient of only one function at each iteration, which is chosen based on a cyclic scheme, and uses the aggregated average gradient of all the functions to approximate the full gradient. We prove that not only...
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