横風気流中での自由噴霧および壁面衝突噴霧の特性

内容の要約広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

Experimental Investigation of Gaseous Emissions and Hydrocarbon Speciation for MF and MTHF Gasoline Blends in DISI Engine

With the increasing shortage of fossil energy, the development of engines urgently requires alternative fuels. Gaseous emissions of a gasoline direct injection spark ignition engine fueled with blends of 2-methylfuran (MF 20% vol. and gasoline 80% vol.) and 2-methyltetrahydrofuran (MTHF 20% vol. and gasoline 80% vol.) were experimentally investigated using Gasmeth FTIR. Experiments were conducted at air-fuel ratio (λ = 1) and at engine speed of 1500 rpm using the fuels optimised spark timing. Effects of fuel injection sweeps (180–280 °CA BTDC) on the emission characteristics of blends were investigated at the intermediate load of 5.5 bar IMEP. Hydrocarbon emission (HC) for gasoline is about 41% and 16% higher compared to MF20 and MTHF20 respectively. Carbon monoxide emission for the fuels increases as the injection timing is retarded but the Nitrogen oxide (NOx) emissions was observed to reduce with the retarded injection timing. Both MF20 and MTHF20 recorded high NOx emissions compared to gasoline. The results indicated ethylene (25–26%) as the major component of the HC speciation in the fuels investigated. The unburnt furan samples for blend fuels were determined to be less than 3% of HC emissions, which could be considered a safe level for exposure

Compressed Wideband Spectrum Sensing Based on Discrete Cosine Transform

Discrete cosine transform (DCT) is a special type of transform which is widely used for compression of speech and image. However, its use for spectrum sensing has not yet received widespread attention. This paper aims to alleviate the sampling requirements of wideband spectrum sensing by utilizing the compressive sampling (CS) principle and exploiting the unique sparsity structure in the DCT domain. Compared with discrete Fourier transform (DFT), wideband communication signal has much sparser representation and easier implementation in DCT domain. Simulation result shows that the proposed DCT-CSS scheme outperforms the conventional DFT-CSS scheme in terms of MSE of reconstruction signal, detection probability, and computational complexity

Compressed Wideband Spectrum Sensing Based on Discrete Cosine Transform

Discrete cosine transform (DCT) is a special type of transform which is widely used for compression of speech and image. However, its use for spectrum sensing has not yet received widespread attention. This paper aims to alleviate the sampling requirements of wideband spectrum sensing by utilizing the compressive sampling (CS) principle and exploiting the unique sparsity structure in the DCT domain. Compared with discrete Fourier transform (DFT), wideband communication signal has much sparser representation and easier implementation in DCT domain. Simulation result shows that the proposed DCT-CSS scheme outperforms the conventional DFT-CSS scheme in terms of MSE of reconstruction signal, detection probability, and computational complexity

Interference Management by Harnessing Multi-Domain Resources in Spectrum-Sharing Aided Satellite-Ground Integrated Networks

A spectrum-sharing satellite-ground integrated network is conceived, consisting of a pair of non-geostationary orbit (NGSO) constellations and multiple terrestrial base stations, which impose the co-frequency interference (CFI) on each other. The CFI may increase upon increasing the number of satellites. To manage the potentially severe interference, we propose to rely on joint multi-domain resource aided interference management (JMDR-IM). Specifically, the coverage overlap of the constellations considered is analyzed. Then, multi-domain resources - including both the beam-domain and power-domain - are jointly utilized for managing the CFI in an overlapping coverage region. This joint resource utilization is performed by relying on our specifically designed beam-shut-off and switching based beam scheduling, as well as on long short-term memory based joint autoregressive moving average assisted deep Q network aided power scheduling. Moreover, the outage probability (OP) of the proposed JMDR-IM scheme is derived, and the asymptotic analysis of the OP is also provided. Our performance evaluations demonstrate the superiority of the proposed JMDR-IM scheme in terms of its increased throughput and reduced OP.Comment: Submitted to IEEE Transactions on Vehicular Technology, Under revie

Parameter sensitivity analysis for diesel spray penetration prediction based on GA-BP neural network

Machine learning has started to be used in engine research to optimize combustion and predict fuel spray characteristics. This paper presents the development of a machine learning model using a Genetic Algorithm-Backpropagation (GA-BP) neural network to predict spray penetration. The GA-BP neural network was selected for its ability to optimize neural network weights and thresholds, thereby improving model convergence and avoiding local minima, which are common challenges in complex, non-linear problems such as spray prediction. The model was trained using experimental data from diesel injector spray tests, and its accuracy was evaluated through parametric sensitivity analysis, examining the influence of various input factors. A comparison between the machine learning model and the traditional empirical formulas of spray penetration revealed that the machine learning model achieved greater accuracy. In terms of the sensitivity to inputs, it is interesting to find that the cognition of machines is different from that of humans. When an input parameter does not have any functional relationship with other input parameters, the absence of this input parameter will lead to a significant decrease in the accuracy of the output result. The results demonstrate that the machine learning approach offers higher accuracy and better generalizability compared to traditional empirical methods. This study recommends the ways to get better results of penetration prediction with BP neural networks, which is efficient in training and utilizing Artificial Neural Networks (ANNs).<br/

Genome-wide promoter extraction and analysis in human, mouse, and rat

Large-scale and high-throughput genomics research needs reliable and comprehensive genome-wide promoter annotation resources. We have conducted a systematic investigation on how to improve mammalian promoter prediction by incorporating both transcript and conservation information. This enabled us to build a better multispecies promoter annotation pipeline and hence to create CSHLmpd (Cold Spring Harbor Laboratory Mammalian Promoter Database) for the biomedical research community, which can act as a starting reference system for more refined functional annotations