Engine fault diagnosis using probabilistic neural network

Engine failure is one of the major factors caused vehicle breakdown. In the current practice, the engine faults are diagnosed manually by mechanics and the accuracy is highly relied on their experience. Therefore, this study would like to explore the feasibility of implementing auto fault diagnosis using Probabilistic Neural Network (PNN). A benchmarked engine fault model is developed and simulated in Maltab. The proposed algorithm is designed to detect 9 common engine faults based on the information extracted from exhaust gas, such as hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx), carbon dioxide (CO2) and dioxygen (O2). The proposed PNN is trained using the collected engine fault data from experiment and the probability density of PNN is determined based on the Parzen window estimation method. Bayes decision rule is implemented for classifying the types of the engine faults. The simulated results show that the proposed algorithm has faster diagnosis speed, higher accuracy and consistent. The algorithm takes 0.038 s in diagnosing the fault and the average accuracy is 98.3 %

Investigation of Semiconductor Quantum Dots for Waveguide Electroabsorption Modulator

In this work, we investigated the use of 10-layer InAs quantum dot (QD) as active region of an electroabsorption modulator (EAM). The QD-EAM is a p-i-n ridge waveguide structure with intrinsic layer thickness of 0.4 μm, width of 10 μm, and length of 1.0 mm. Photocurrent measurement reveals a Stark shift of ~5 meV (~7 nm) at reverse bias of 3 V (75 kV/cm) and broadening of the resonance peak due to field ionization of electrons and holes was observed for E-field larger than 25 kV/cm. Investigation at wavelength range of 1,300–1320 nm reveals that the largest absorption change occurs at 1317 nm. Optical transmission measurement at this wavelength shows insertion loss of ~8 dB, and extinction ratio of ~5 dB at reverse bias of 5 V. Consequently, methods to improve the performance of the QD-EAM are proposed. We believe that QDs are promising for EAM and the performance of QD-EAM will improve with increasing research efforts

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

TiO2 thin films by APCVD for photocatalytic applications

Titanium dioxide thin film was deposited on glass using in-house built Atmospheric Pressure Chemical Vapor Deposition. The system was capable of depositing uniform coating on a large substrate and can be scaled-up easily for industrial applications. The deposited film consists of photocatalytic anatase phase and was shown to degrade both stearic acid and ethyl cellulose contaminations that were deposited on top of the films. Various modifications were carried out to improve the UV and visible light photo-response of the TiO2 film; thermal annealing, embedding photocatalytic particles (Anatase, rutile, P25 and SrTi(1-x)FexO3), tin-doping and deposition of TiO2/SnO2 bilayer. Among these various modifications, embedding P25 nanoparticles and-tin doping showed enhancement to the degradation rate of stearic acid under UV illumination. Other modifications suffered from a loss of photoactivity cause primarily by the introduction of recombination centers and increased in grain size.DOCTOR OF PHILOSOPHY (EEE

+65-6874-1948

A novel maximal figure-of-merit (MFoM) learning approach to text categorization is proposed. Different from the conventional techniques, the proposed MFoM method attempts to integrate any performance metric of interest (e.g. accuracy, recall, precision, or F 1 measure) into the design of any classifier. The corresponding classifier parameters are learned by optimizing an overall objective function of interest. To solve this highly nonlinear optimization problem, we use a generalized probabilistic descent algorithm. The MFoM learning framework is evaluated on the Reuters-21578 task with LSI-based feature extraction and a binary tree classifier. Experimental results indicate that the MFoM classifier gives improved F 1 and enhanced robustness over the conventional one. It also outperforms the popular SVM method in micro-averaging F 1. Other extensions to design discriminative multiple-category MFoM classifiers for application scenarios with new performance metrics could be envisioned too

A review on multifunctional bioceramic coatings in hip implants for osteointegration enhancement

As demand for orthopedic surgery increases, more modern ceramic materials have been developed progressively. Bioceramic coating has been the center of focus to tackle poor osteointegration and complications resulting from the metallic stem and femoral head. The ability to form a passivation layer, protection against corrosion tendency, and prevention of wear particles leading to osteolysis, are some of the characteristics required within the hip replacement surgery. Promising coatings such as diamond-like carbon and titanium nitride materials have undergone successful clinical trials as well as deposition methods to tailor their chemical and structural properties. The aim of this review paper is to give an insight into hip replacement materials and related properties. The importance of the tribocorrosion effect and its electrochemical fundamentals are also examined to study the viable coating deposition methods. Both careful selections of implant materials and deposition methods are vital to maximize the osteointegration capability, whether application is used in bearing articulation (ball-on-socket) or femoral stem

ABSTRACT

A novel maximal figure-of-merit (MFoM) learning approach to text categorization is proposed. Different from the conventional techniques, the proposed MFoM method attempts to integrate any performance metric of interest (e.g. accuracy, recall, precision, or F1 measure) into the design of any classifier. The corresponding classifier parameters are learned by optimizing an overall objective function of interest. To solve this highly nonlinear optimization problem, we use a generalized probabilistic descent algorithm. The MFoM learning framework is evaluated on the Reuters-21578 task with LSI-based feature extraction and a binary tree classifier. Experimental results indicate that the MFoM classifier gives improved F1 and enhanced robustness over the conventional one. It also outperforms the popular SVM method in micro-averaging F1. Other extensions to designing discriminative multiplecategory MFoM classifiers for application scenarios with new performance metrics could be envisioned too. 1