Droop-controlled dc microgrids are widely used in the field of renewable energy generation. In order to endow fast transient response to the source converters, current-mode droop control approaches are preferred rather than the voltage-mode counterparts. However, a small droop gain in current-mode droop-control will bring instability challenges to the system. In this paper, a novel impedance-shaping based active damping technique is proposed to facilitate better stability performance of the current-mode droop-controlled system. A detailed impedance model is established to analyze the system stability under different operating conditions. The theoretical results obtained in this paper are validated by simulation results with switching models.

Considered as an important option to enhance power distribution capacity, improve operation flexibility and increase power quality in distribution grids, the medium-voltage DC (MVDC) technology has gained increasing interests in recent years. In this paper, the state of the art of researches and applications of MVDC distribution system is presented. Main topics and progresses are explored including planning and evaluation, main circuits, key equipment, control and protection. The most recent pilot projects around the world are introduced among which the Tangjia Bay pilot project with the largest capacity is illustrated in detail, together with the practical issues during the design, experiment, and commissioning.

Activities and events in our lives are structural, be it a vacation, a camping trip, or a wedding.While individual details vary, there are characteristic patterns that are specific to each of these scenarios.For example, a wedding typically consists of a sequence of events such as walking down the aisle, exchanging vows, and dancing.In this paper, we present a data-driven approach to learning event knowledge from a large collection of photo albums.We formulate the task as constrained optimization to induce the prototypical temporal structure of an event, integrating both visual and textual cues.Comprehensive evaluation demonstrates that it is possible to learn multimodal knowledge of event structure from noisy web content.

Jianfu Chen, Polina Kuznetsova, David Warren, Yejin Choi. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

We study hierarchical classification of products in electronic commerce, classifying a text description of a product into one of the leaf classes of a tree-structure taxonomy. In particular, we investigate two essential problems, performance evaluation and learning, in a synergistic way. Unless we know what is the appropriate performance evaluation metric for a task, we are not going to learn a classifier of maximum utility for the task. Given the characteristics of the task of hierarchical product classification, we shed insight into how and why common evaluation metrics such as error rate can be misleading, which is applicable for treating other real world applications. The analysis leads to a new performance evaluation metric that tailors this task to reflect a vendor's business goal of maximizing revenue. The proposed metric has an intuitive meaning as the average revenue loss, which depends on both the value of individual products and the hierarchical distance between the true class and the predicted class. Correspondingly, our learning algorithm uses multi-class SVM with margin re-scaling to optimize the proposed metric, instead of error rate or other common metrics. Margin re-scaling is sensitive to the scaling of loss functions. We propose a loss normalization approach to appropriately calibrating the scaling of loss functions, which is applicable to general classification and structured prediction tasks whenever using structured SVM with margin re-scaling. Experiments on a large dataset show that our approach outperforms standard multi-class SVM in terms of the proposed metric, effectively reducing the average revenue loss.

Why do certain combinations of words such as "disadvantageous peace" or "metal to the petal" appeal to our minds as interesting expressions with a sense of creativity, while other phrases such as "quiet teenager", or "geometrical base" not as much?We present statistical explorations to understand the characteristics of lexical compositions that give rise to the perception of being original, interesting, and at times even artistic.We first examine various correlates of perceived creativity based on information theoretic measures and the connotation of words, then present experiments based on supervised learning that give us further insights on how different aspects of lexical composition collectively contribute to the perceived creativity.