Optimal Modeled Six-Phase Space Vector Pulse Width Modulation Method for Stator Voltage Harmonic Suppression

Dual Y shift 30 six-phase motors are expected to be extensively applied in high-power yet energy-effective fields, and a harmonic-suppressing control strategy plays a vital role in extending their prominent features of low losses and ultra-quiet operation. Aiming at the suppression of harmonic voltages, this paper proposes a six-phase space vector pulse width modulation method based on an optimization model, namely OM-SVPWM. First, four adjacent large vectors are employed in each of 12 sectors on a fundamental sub-plane. Second, the optimization model is constructed to intelligently determine activation durations of the four vectors, where its objective function aims to minimize the synthesis result on a harmonic sub-plane, and its constraint condition is that the synthesis result on the fundamental sub-plane satisfies a reference vector. Finally, to meet the real-time requirement, optimum solutions are obtained by using general central path following algorithm (GCPFA). Simulation and experiment results prove that, the OM-SVPWM performs around 37% better than a state-of-the-art competitive SVPWM in terms of harmonics suppression, which promise the proposed OM-SVPWM conforms to the energy-effective direction in actual engineering applications.Peer reviewe

Efficient detection of contagious outbreaks in massive metropolitan encounter networks

Physical contact remains difficult to trace in large metropolitan networks, though it is a key vehicle for the transmission of contagious outbreaks. Co-presence encounters during daily transit use provide us with a city-scale time-resolved physical contact network, consisting of 1 billion contacts among 3 million transit users. Here, we study the advantage that knowledge of such co-presence structures may provide for early detection of contagious outbreaks. We first examine the "friend sensor" scheme --- a simple, but universal strategy requiring only local information --- and demonstrate that it provides significant early detection of simulated outbreaks. Taking advantage of the full network structure, we then identify advanced "global sensor sets", obtaining substantial early warning times savings over the friends sensor scheme. Individuals with highest number of encounters are the most efficient sensors, with performance comparable to individuals with the highest travel frequency, exploratory behavior and structural centrality. An efficiency balance emerges when testing the dependency on sensor size and evaluating sensor reliability; we find that substantial and reliable lead-time could be attained by monitoring only 0.01% of the population with the highest degree.Comment: 4 figure

Two-dimensional approximately harmonic projection for gait recognition

This paper presents a two-dimensional approximately harmonic projection (2DAHP) algorithm for gait recognition. 2DAHP is originated from the approximately harmonic projection (AHP), while 2DAHP offers some advantages over AHP. 1) 2DAHP can preserve the local geometrical structure and cluster structure of image data as AHP. 2) 2DAHP encodes images as matrices or second-order tensors rather than one-dimensional vectors, so 2DAHP can keep the correlation among different coordinates of image data. 3) 2DAHP avoids the singularity problem suffered by AHP. 4) 2DAHP runs faster than AHP. Extensive experiments on gait recognition show the effectiveness and efficiency of the proposed method

RBX1/ROC1-SCF E3 ubiquitin ligase is required for mouse embryogenesis and cancer cell survival

RBX1 (also known as ROC1) is a RING subunit of SCF (Skp1, Cullins, F-box proteins) E3 ubiquitin ligases, required for SCF to direct a timely degradation of diverse substrates, thereby regulating numerous cellular processes under both physiological and pathological conditions. Previous studies have shown that RBX1 is essential for growth in yeast, Caenorhabditis elegans and Drosophila. The role of RBX1 in mouse development and in regulation of cancer cell survival was unknown. Our recent work demonstrated that RBX1 is an essential gene for mouse embryogenesis, and targeted disruption of RBX1 causes embryonic lethality at E7.5 due to hypoproliferation as a result of p27 accumulation. We also showed that RBX1 is overexpressed in a number of human cancers, and siRNA silencing of RBX1 caused cancer cell death as a result of sequential induction of G2-M arrest, senescence and apoptosis. These findings reveal a physiological role of RBX1 during mouse development and a pathological role for the survival of human cancer cells. Differential outcomes between normal (growth arrest) and cancer cells (cell death) upon RBX1 disruption/silencing suggest RBX1 as a valid anticancer target

Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis

Highway traffic states data collected from a network of sensors can be considered a high-dimensional nonlinear dynamical system. In this paper, we develop a novel data-driven method -- anti-circulant dynamic mode decomposition with sparsity-promoting (circDMDsp) -- to study the dynamics of highway traffic speed data. Particularly, circDMDsp addresses several issues that hinder the application of existing DMD models: limited spatial dimension, presence of both recurrent and non-recurrent patterns, high level of noise, and known mode stability. The proposed circDMDsp framework allows us to numerically extract spatial-temporal coherent structures with physical meanings/interpretations: the dynamic modes reflect coherent spatial bases, and the corresponding temporal patterns capture the temporal oscillation/evolution of these dynamic modes. Our result based on Seattle highway loop detector data showcases that traffic speed data is governed by a set of periodic components, e.g., mean pattern, daily pattern, and weekly pattern, and each of them has a unique spatial structure. The spatiotemporal patterns can also be used to recover/denoise observed data and predict future values at any timestamp by extrapolating the temporal Vandermonde matrix. Our experiments also demonstrate that the proposed circDMDsp framework is more accurate and robust in data reconstruction and prediction than other DMD-based models

Bayesian Calibration of the Intelligent Driver Model

Accurate calibration of car-following models is essential for investigating microscopic human driving behaviors. This work proposes a memory-augmented Bayesian calibration approach, which leverages the Bayesian inference and stochastic processes (i.e., Gaussian processes) to calibrate an unbiased car-following model while extracting the serial correlations of residual. This calibration approach is applied to the intelligent driver model (IDM) and develops a novel model named MA-IDM. To evaluate the effectiveness of the developed approach, three models with different hierarchies (i.e., pooled, hierarchical, and unpooled) are tested. Experiments demonstrate that the MA-IDM can estimate the noise level of unrelated errors by decoupling the serial correlation of residuals. Furthermore, a stochastic simulation method is also developed based on our Bayesian calibration approach, which can obtain unbiased posterior motion states and generate anthropomorphic driving behaviors. Simulation results indicate that the MA-IDM outperforms Bayesian IDM in simulation accuracy and uncertainty quantification. With this Bayesian approach, we can generate enormous but nonidentical driving behaviors by sampling from the posteriors, which can help develop a realistic traffic simulator