Discontinuous pulse-width modulation (DPWM) of neutral-point-clamped (NPC)NPC three-level inverter can effectively reduce a large amount of switching losses in high-frequency applications by controlling single phase switch to not operate within 1/3 carrier period. However, the traditional over-zero clamped type DPWM (DPWMA) strategy with unchangeable clamped interval has high low-order harmonics in output current contains, and has poor neutral-point voltage balancing ability with a single redundant small vector. A new carrier-based DPWM (CB-DPWM) scheme is proposed to decrease the current low-order harmonics and optimize neutral-point voltage balancing ability, which has variable clamped interval. The proposed CB-DPWM method judges the relationship between the three-phase sine modulation wave and bias voltage after left and right phase shifting, and has adjustable zero crossing and peak clamped interval. Simulation and experimental verification were carried out on a three-level NPC inverter platform, verifying the correctness of the theory. Compared with DPWMA, the proposed method has better output current quality, better neutral-point voltage balancing ability and dynamic performance.

A probabilistic model for the predicted power sources is typically assumed for the existing uncertain optimal power flow (UOPF). However, obtaining accurate information of that is very challenging in practice due to the limited available data of renewable energy and loads with complex correlations. To address that, this article proposes a belief function based framework for UOPF with a large number of uncertain power sources. Multiple imperfect models with q-least committed joint basic belief density are developed to characterize the knowledge deficient power sources. This yields the integration of the generalized Bayesian theorem with the traditional evidence theory into a unified manner. The former allows estimating the uncertain model of the predicted power sources, whereas the latter is to obtain the probability box of the UOPF variables. Comparison results with the Monte Carlo simulations and the three-point estimation approach show that the proposed method is able to get accurate UOPF results while achieving high computational efficiency for large-scale systems with a large number of knowledge deficient power sources.

Osteoarthritis (OA) is the most common joint disease worldwide, resulting in severe joint pain and significantly decreased life quality in the elderly population. Due to the lack of effective medicine that can reverse the degeneration of articular cartilage, ultimately all OA patients need to receive an artificial joint replacement surgery. Although the pathogenesis and progression of OA has attracted a lot research activities, most regulation mechanisms at genetic and genomics level have not yet been completely understood. In this paper, we introduce the utilization of OmniSearch, a semantic search software tool, to facilitate the exploration of important roles performed by numerous microRNA molecules on OA disease process. Our promising experimental results have indicated that the methodologies described in this paper are able to help effectively unraveling critical microRNA regulation mechanisms. Consequently, we can facilitate clinical investigators' efforts in the understanding of OA pathogenesis, and thus contributing to early diagnosis and effective treatment in OA in the future.

Although a number of normalized edit distances presented so far may offer good performance in some applications, none of them can be regarded as a genuine metric between strings because they do not satisfy the triangle inequality. Given two strings X and Y over a finite alphabet, this paper defines a new normalized edit distance between X and Y as a simple function of their lengths (|X| and |Y|) and the Generalized Levenshtein Distance (GLD) between them. The new distance can be easily computed through GLD with a complexity of O(|X|.|Y|) and it is a metric valued in [0, 1] under the condition that the weight function is a metric over the set of elementary edit operations with all costs of insertions/deletions having the same weight. Experiments using the AESA algorithm in handwritten digit recognition show that the new distance can generally provide similar results to some other normalized edit distances and may perform slightly better if the triangle inequality is violated in a particular data set.