「ノイズ」を含んだ数値解析による実車両の効率的空力形状最適化手法の改良

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

API High Speed Balancing Acceptance Criteria and Pedestal Dynamics

LectureAcceptance criteria for high-speed balancing of turbomachinery are specified in API standards based on either pedestal velocity or shaft displacement. In addition to performing balancing, the measured displacements can also be used for verification of the unbalance response analysis. Since the pedestals are relatively soft, their dynamics need to be considered in the analysis. In this paper, multiple modal tests were conducted on 3 different pedestals. Different torques on the pedestal bolts were used to study the effect on the measured FRFs. The added-mass method was applied to DH7 pedestals. The calculated modal mass and stiffness were compared to values identified from the measured FRFs. Unbalance verification of some shop orders is compared to the predictions with different ways of characterizing the pedestal dynamics: rigid, mass and stiffness, and the FRFs

Scholars' data reuse behaviors in disciplinary context: A meta-synthesis study

Data reuse plays a pivotal role in science research in the data era. Given that the impact of discipline culture on data reuse is deeply rooted, we explore data reuse behaviors of the two groups of scholars with significantly different qualities, the nature science and the humanities and social science. Relying on the meta-synthesis and inductive coding approach, information about intentions, influence factors, data processing and using and data reuse barriers were extracted from 37 qualified articles and then analyzed. Results show: 1) informal channels perform a vital role in data reuse in both two communities; 2) there is a distinct correlation between data reuse and disciplinary context. 3) clear distinctions exist between two fields in data reuse barriers, disciplinary practice degrees and data reuse patterns. The results imply the urgency to establish data managers, link publications and data, and enhance data organization

ATMOSPHERIC VARIATIONS IN COLUMN INTEGRATED CO2 ON SYNOPTIC AND SEASONAL TIME SCALE OVER THE U.S.

Past studies have demonstrated that synoptic events play an important role in the spatial and temporal variations of carbon dioxide (CO2). In this study, in order to investigate whether cold fronts have impact on synoptic concentrations, we collect 83 cold frontal cases over United Sates, east Pacific Ocean and west Atlantic Ocean (or the contiguous United States: CONUS) from 2015 to 2017 with data from Orbiting Carbon Observatory-2 (OCO-2) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), calculate the Column-averaged carbon dioxide dry air mole fraction (XCO2) difference anomalies across the fronts, and apply significance test in each season based on non-frontal days climatology to decide whether cold fronts relate to horizontal changes. The large day-to-day variability at the same location from Weather Research and Forecasting- Vegetation Photosynthesis and Respiration Model (WRF-VPRM) suggest that randomly selected orbits which are not crossing any fronts are a better reference than monthly mean on the same spatial resolution in one month. Seeing that OCO-2 measures well on mesoscale and synoptic scale, we regard OCO-2 data as the truth and examine whether simulations from WRF-VRPM are simulating well over the contiguous United States (CONUS). Based on land cover classifications given by the Moderate Resolution Imaging Spectroradiometer (MODIS), we compute the difference between the model simulations and OCO-2 observations for each land cover type in each season. The evaluation reveals that the model agrees well with OCO-2 for some specific land cover and season, like forests in the winter, while still relative high bias in summer for most surface types. In summary, the result that frontal gradients have similar pattern with past studies in boundary layer demonstrates that OCO-2 is a good tool to see mesoscale or synoptic and seasonal variations over the CONUS. Accordingly, regarding OCO-2 data as the truth, outputs from WRF-VPRM are evaluated based on 7 land cover types, which was proved generally well in winter over some land cover types, but may not ideal for others

Attention-free Spikformer: Mixing Spike Sequences with Simple Linear Transforms

By integrating the self-attention capability and the biological properties of Spiking Neural Networks (SNNs), Spikformer applies the flourishing Transformer architecture to SNNs design. It introduces a Spiking Self-Attention (SSA) module to mix sparse visual features using spike-form Query, Key, and Value, resulting in the State-Of-The-Art (SOTA) performance on numerous datasets compared to previous SNN-like frameworks. In this paper, we demonstrate that the Spikformer architecture can be accelerated by replacing the SSA with an unparameterized Linear Transform (LT) such as Fourier and Wavelet transforms. These transforms are utilized to mix spike sequences, reducing the quadratic time complexity to log-linear time complexity. They alternate between the frequency and time domains to extract sparse visual features, showcasing powerful performance and efficiency. We conduct extensive experiments on image classification using both neuromorphic and static datasets. The results indicate that compared to the SOTA Spikformer with SSA, Spikformer with LT achieves higher Top-1 accuracy on neuromorphic datasets (i.e., CIFAR10-DVS and DVS128 Gesture) and comparable Top-1 accuracy on static datasets (i.e., CIFAR-10 and CIFAR-100). Furthermore, Spikformer with LT achieves approximately 29-51% improvement in training speed, 61-70% improvement in inference speed, and reduces memory usage by 4-26% due to not requiring learnable parameters.Comment: Under Revie