DFMA-based design guidelines for high-rise modular buildings

This research aims to develop DfMA-based design guidelines for high-rise modular buildings to facilitate the widespread application and development of innovative OSC technology. This research contributes to linking the DfMA philosophy with the constructability of high-rise modular buildings in terms of manufacturability, transportability and assemblability

First Total Synthesis of a Naturally Occurring Iodinated 5′-Deoxyxylofuranosyl Marine Nucleoside

4-Amino-7-(5′-deoxy-β-D-xylofuranosyl)-5-iodo-pyrrolo[2,3-d]pyrimidine 1, an unusual naturally occurring marine nucleoside isolated from an ascidan, Diplosoma sp., was synthesized from D-xylose in seven steps with 28% overall yield on 10 g scale. The key step was Vorbrüggen glycosylation of 5-iodo-pyrrolo[2,3-d]pyrimidine with 5-deoxy-1,2-O-diacetyl-3-O-benzoyl-D-xylofuranose. Its absolute configuration was confirmed

Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data

Cost-effective wind turbine diagnosis using SCADA data is a promising technology for future intelligent wind farm operation and management. This paper presents a thermophysics based method for wind turbine drivetrain fault diagnosis. A synthesized thermal model is formed by incorporating thermal mechanisms of the drivetrain into a wind turbine system model. Applications of the model are demonstrated in case studies of the gearbox and generator comparing simulation results and SCADA data analysis. The results show nonlinearity of the gearbox oil temperature rise with wind speed/output power that can effectively indicate gearbox efficiency degradation, which may be attributed to gear transmission problems such as gear teeth wear. Electrical generator faults, such as ventilation failure and winding voltage unbalance will cause changes to heat transfer and result in temperature changes that can be used for diagnosis purposes. This is shown by different patterns of stator winding temperature associated with power generation, while the simulation reveals the thermal mechanism. The method can be applied to diagnose some failure modes which are hard to identify from vibration analysis. The developed thermal model can play a central role for the purpose of fault diagnosis, by deriving relationships between various SCADA signals and revealing changes in the thermophysics of wind turbine operation

Node Copying: A Random Graph Model for Effective Graph Sampling

There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative of the true relationship amongst nodes. In these settings, building a generative model conditioned on the observed graph allows to take the graph uncertainty into account. Various existing techniques either rely on restrictive assumptions, fail to preserve topological properties within the samples or are prohibitively expensive for larger graphs. In this work, we introduce the node copying model for constructing a distribution over graphs. Sampling of a random graph is carried out by replacing each node's neighbors by those of a randomly sampled similar node. The sampled graphs preserve key characteristics of the graph structure without explicitly targeting them. Additionally, sampling from this model is extremely simple and scales linearly with the nodes. We show the usefulness of the copying model in three tasks. First, in node classification, a Bayesian formulation based on node copying achieves higher accuracy in sparse data settings. Second, we employ our proposed model to mitigate the effect of adversarial attacks on the graph topology. Last, incorporation of the model in a recommendation system setting improves recall over state-of-the-art methods

Conceptual Study of a Real-Time Hybrid Simulation Framework for Monopile Offshore Wind Turbines Under Wind and Wave Loads

As an attractive renewable energy source, offshore wind plants are becoming increasingly popular for energy production. However, the performance assessment of offshore wind turbine (OWT) structure is a challenging task due to the combined wind-wave loading and difficulties in reproducing such loading conditions in laboratory. Real-time hybrid simulation (RTHS), combining physical testing and numerical simulation in real-time, offers a new venue to study the structural behavior of OWTs. It overcomes the scaling incompatibilities in OWT scaled model testing by replacing the rotor components with an actuation system, driven by an aerodynamic simulation tool running in real-time. In this study, a RTHS framework for monopile OWTs is proposed. A set of sensitivity analyses is carried out to evaluate the feasibility of this RTHS framework and determine possible tolerances on its design. By simulating different scaling laws and possible error contributors (delays and noises) in the proposed framework, the sensitivity of the OWT responses to these parameters are quantified. An example using a National Renewable Energy Lab (NREL) 5-MW reference OWT system at 1:25 scale is simulated in this study to demonstrate the proposed RTHS framework and sensitivity analyses. Three different scaling laws are considered. The sensitivity results show that the delays in the RTHS framework significantly impact the performance on the response evaluation, higher than the impact of noises. The proposed framework and sensitivity analyses presented in this study provides important information for future implementation and further development of the RTHS technology for similar marine structures

PSSA: PCA-domain superpixelwise singular spectral analysis for unsupervised hyperspectral image classification.

Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsupervised HSI classification based on data clustering is highly desired, yet it generally suffers from high computational cost and low classification accuracy, especially in large datasets. To tackle these challenges, a novel unsupervised spatial-spectral HSI classification method is proposed. By combining the entropy rate superpixel segmentation (ERS), superpixel-based principal component analysis (PCA), and PCA-domain 2D singular spectral analysis (SSA), both the efficacy and efficiency of feature extraction are improved, followed by the anchor-based graph clustering (AGC) for effective classification. Experiments on three publicly available and five self-collected aerial HSI datasets have fully demonstrated the efficacy of the proposed PCA-domain superpixelwise SSA (PSSA) method, with a gain of 15–20% in terms of the overall accuracy, in comparison to a few state-of-the-art methods. In addition, as an extra outcome, the HSI dataset we acquired is provided freely online