Deviation warnings of ferries based on artificial potential field and historical data

Ferries are usually used for transporting passengers and vehicles among docks, and any deviation of the course can lead to serious consequences. Therefore, transportation ferries must be watched closely by local maritime administrators, which involves much manpower. With the use of historical data, this article proposes an intelligent method of integrating artificial potential field with Bayesian Network to trigger deviation warnings for a ferry based on its trajectory, speed and course. More specifically, a repulsive potential field-based model is first established to capture a customary waterway of ferries. Subsequently, a method based on non-linear optimisation is introduced to train the coefficients of the proposed repulsive potential field. The deviation of a ferry from the customary route can then be quantified by the potential field. Bayesian Network is further introduced to trigger deviation warnings in accordance with the distribution of deviation values, speeds and courses. Finally, the proposed approach is validated by the historical data of a chosen ferry on a specific route. The testing results show that the approach is capable of providing deviation warnings for ferries accurately and can offer a practical solution for maritime supervision. © IMechE 2019

Machinery Early Fault Detection Based on Dirichlet Process Mixture Model

© 2013 IEEE. The most commonly used single feature-based anomaly detection method for the complex machinery, such as large wind power equipment, steam turbine generator sets, and reciprocating compressors, exhibits a defect of low-alarm accuracy due to the non-stationary characteristic of the vibration signals. In order to improve the accuracy of fault detection, a novel method based on the Dirichlet process mixture model (DPMM) is proposed. First, the features of the mechanical vibration signals are used to construct the feature space of the equipment. The DPMM modeling method is then applied to self-learn the probabilistic mixture model of the feature space. The normal working condition model is used as the benchmark model. The early fault detection is realized by using a precise difference measurement method based on Kullback-Leibler divergence to calculate the difference between the real-time model and the benchmark model accurately, and by comparing the calculation result with a self-learned alarm threshold. The effectiveness and the adaptability of this novel early fault detection method are verified by comparing it to the single feature-based anomaly detection method and the Gaussian mixture model (GMM)-based early fault detection method

Micromagnetic simulations of current-induced magnetization switching in Co/Cu/Co nanopillars

Author name used in this publication: S. Q. Shi2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Climate and soil moisture content during development ofthe frst palaeosol in the southern Loess Plateau

The scientific problems concerning Quaternary soil water content and the water cycle have not been researched. This study examined the soil water content and depth of distribution of gravitational water in the south Loess Plateau during development of the first palaeosol layer (S1) by methods such as field investigation, electron microscopy, energy spectrum analysis, chemical analysis, and so on. The purpose was to reveal the climate, water balance and vegetation type at the time when S1 developed. The depth of migration of CaCO3 and Sr were 4.2 m below the upper boundary of the S1 palaeosol, and the depth of weathered loess beneath the argillic horizon was 4.0 m. Ferri‐argillans developed well in the argillic horizon and their depth of migration was 1 m below the argillic horizon. These findings suggest that the climate during the last interglacial was subtropical and humid, and the soil‐water balance was positive. Gravitational water was present to a depth of least 4.2 m from the top of S1, and the water content was adequate for tree growth. The chemical weathering index showed that this palaeosol has been moderately weathered

Current-induced magnetization dynamics in Co/Cu/Co nanopillars

Author name used in this publication: S. Q. Shi2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Elastoplastic phase field model for microstructure evolution

2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

GPR43 deficiency protects against podocyte insulin resistance in diabetic nephropathy through the restoration of AMPKα activity

RATIONALE: Albuminuria is an early clinical feature in the progression of diabetic nephropathy (DN). Podocyte insulin resistance is a main cause of podocyte injury, playing crucial roles by contributing to albuminuria in early DN. G protein-coupled receptor 43 (GPR43) is a metabolite sensor modulating the cell signalling pathways to maintain metabolic homeostasis. However, the roles of GPR43 in podocyte insulin resistance and its potential mechanisms in the development of DN are unclear. METHODS: The experiments were conducted by using kidney tissues from biopsied DN patients, streptozotocin (STZ) induced diabetic mice with or without global GPR43 gene knockout, diabetic rats treated with broad-spectrum oral antibiotics or fecal microbiota transplantation, and cell culture model of podocytes. Renal pathological injuries were evaluated by periodic acid-schiff staining and transmission electron microscopy. The expression of GPR43 with other podocyte insulin resistance related molecules was checked by immunofluorescent staining, real-time PCR, and Western blotting. Serum acetate level was examined by gas chromatographic analysis. The distribution of gut microbiota was measured by 16S ribosomal DNA sequencing with faeces. RESULTS: Our results demonstrated that GPR43 expression was increased in kidney samples of DN patients, diabetic animal models, and high glucose-stimulated podocytes. Interestingly, deletion of GPR43 alleviated albuminuria and renal injury in diabetic mice. Pharmacological inhibition and knockdown of GPR43 expression in podocytes increased insulin-induced Akt phosphorylation through the restoration of adenosine 5'-monophosphate-activated protein kinase α (AMPKα) activity. This effect was associated with the suppression of AMPKα activity through post-transcriptional phosphorylation via the protein kinase C-phospholipase C (PKC-PLC) pathway. Antibiotic treatment-mediated gut microbiota depletion, and faecal microbiota transplantation from the healthy donor controls substantially improved podocyte insulin sensitivity and attenuated glomerular injury in diabetic rats accompanied by the downregulation of the GPR43 expression and a decrease in the level of serum acetate. CONCLUSION: These findings suggested that dysbiosis of gut microbiota-modulated GPR43 activation contributed to albuminuria in DN, which could be mediated by podocyte insulin resistance through the inhibition of AMPKα activity

Stabilizing forces acting on ZnO polar surfaces: STM, LEED, and DFT

published_or_final_versio

Trace doping of multiple elements enables stable battery cycling of LiCoO2 at 4.6 V

LiCoO2 is a dominant cathode material for lithium-ion (Li-ion) batteries due to its high volumetric energy density, which could potentially be further improved by charging to high voltages. However, practical adoption of high-voltage charging is hindered by LiCoO2’s structural instability at the deeply delithiated state and the associated safety concerns. Here, we achieve stable cycling of LiCoO2 at 4.6 V (versus Li/Li+) through trace Ti–Mg–Al co-doping. Using state-of-the-art synchrotron X-ray imaging and spectroscopic techniques, we report the incorporation of Mg and Al into the LiCoO2 lattice, which inhibits the undesired phase transition at voltages above 4.5 V. We also show that, even in trace amounts, Ti segregates significantly at grain boundaries and on the surface, modifying the microstructure of the particles while stabilizing the surface oxygen at high voltages. These dopants contribute through different mechanisms and synergistically promote the cycle stability of LiCoO2 at 4.6 V