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This paper proposes a new decentralized data-driven load restoration (DDLR) scheme for transmission and distribution (TD) systems with high penetration of wind power. Robust DDLR models are constructed in order to handle uncertainties and ensure the feasibility of decentralized schemes. The Wasserstein metric is used to describe the ambiguity sets of probability distributions in order to build the complete DDLR model and realize computationally tractable formulation. A data-driven model-nested analytical target cascading (DATC) algorithm is developed to obtain the final load restoration result by iteratively solving small-scale mathematical models. The proposed DDLR scheme provides load restoration results with adjustable robustness, and performance efficiency is independent from the amount of data. The DDLR scheme makes full use of the available data while respecting information privacy requirements of independent operated systems, and ensures the feasibility of a decentralized load restoration strategy even in the worst-case condition. The effectiveness of the proposed method is validated using a small-scale TD system and a large-scale system with the IEEE 118-bus TS and thirty IEEE-33 DSs, showing high computational efficiency and superior restoration performance. [ABSTRACT FROM AUTHOR]

This paper focuses on the optimization of a linear actuator by the “output space mapping (OSM)” method. The underlying objective of this work lies in the minimization of the time required for the achievement of this optimal design. Indeed, in addition to the sole costs of optimization processes strictly speaking, the time needed for the developpement of the models is taken into account. In the context of OSM, two different finite-element models of the same actuator are used. This paper presents these modeling solutions and considers their corresponding accuracy. Results of this multi-objective optimization method are presented and compared with those obtained by the sequential simplex (SS) method based solely on the fine model. Both approaches give similar results. However, the comparison of their performances clearly shows that the OSM algorithm is an effective technique for reducing the computation time of optimization studies, even in the case of relatively simple electromagnetic structures. Hence, this approach leads to an original and effective optimization methodology. [ABSTRACT FROM AUTHOR]

IC3 is one of the most successful algorithms for hardware model checking. Since its invention in 2010, several variants of the original algorithm have been published, proposing optimizations and/or alternative procedures for many different steps of the algorithm. In this paper, we present a thorough empirical comparison of a large set of optimizations and procedures for the steps of IC3, considering “high-level” variants/extensions to the basic algorithm, as well as “low-level” optimizations/configuration settings. We implemented each of them in the same tool, optimizing the implementations to the best of our knowledge. This enabled for a flexible experimentation in a controlled environment, and to gain new insights about their most important differences and commonalities, as well as about their performance characteristics. We conducted the experiments using as benchmarks the problems used in the last four editions of the hardware model checking competition. The analysis helped us to identify several settings leading to significant improvements with respect to a basic implementation of IC3. [ABSTRACT FROM AUTHOR]

Groupwise nonrigid image registration is a powerful tool to automatically establish correspondences across sets of images. Such correspondences are widely used for constructing statistical models of shape and appearance. As existing techniques usually treat registration as an optimization problem, a good initialization is required. Although the standard initialization—affine transformation—generally works well, it is often inadequate when registering images of complex structures. In this paper we present a more sophisticated method that uses the sparse matches of a parts+geometry model as the initialization. We show that both the model and its matches can be automatically obtained, and that the matches are able to effectively initialize a groupwise nonrigid registration algorithm, leading to accurate dense correspondences. We also show that the dense mesh models constructed during the groupwise registration process can be used to accurately annotate new images. We demonstrate the efficacy of the approach on three datasets of increasing difficulty, and report on a detailed quantitative evaluation of its performance. [ABSTRACT FROM PUBLISHER]