Design, Modeling and Development of a Serial Hybrid Motorcycle with HCCI Engine

This paper discusses the design, modeling, and development of small motorcycle equipped with a HCCI engine in an series hybrid configuration. A mathematical model was developed using MATLAB/Simulink and used to size the powertrain components and to predict fuel economy. A conventional 125 cc spark ignition engine was modified to run in HCCI combustion mode and integrated into a prototype vehicle. Dual-fuel and external EGR strategies were used to upgrade the engine speed and torque capabilities of the engine to meet the requirements of the powertrain. An electrical generator, hub-motor, battery pack and other power electronics devices were used to form the electrical system for the vehicle. The advantages of the proposed design compared to the original motorcycle with SI engine and CVT transmission are: 1) a reduction in noxious emissions due to the HCCI combustion, and 2) higher fuel economy in city driving because of the HCCI engine and series hybrid powertrain. Fuel economy was measured by driving the motorcycle on a chassis dynamometer using a sequence of ECE-40 driving cycles. The overall fuel economy was measured to be 73.7km/L which represents a 139.3% increase in fuel economy over the baseline vehicle

Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning

Train station parking (TSP) accuracy is important to enhance the efficiency of train operation and the safety of passengers for urban rail transit. However, TSP is always subject to a series of uncertain factors such as extreme weather and uncertain conditions of rail track resistances. To increase the parking accuracy, robustness, and self-learning ability, we propose new train station parking frameworks by using the reinforcement learning (RL) theory combined with the information of balises. Three algorithms were developed, involving a stochastic optimal selection algorithm (SOSA), a Q-learning algorithm (QLA), and a fuzzy function based Q-learning algorithm (FQLA) in order to reduce the parking error in urban rail transit. Meanwhile, five braking rates are adopted as the action vector of the three algorithms and some statistical indices are developed to evaluate parking errors. Simulation results based on real-world data show that the parking errors of the three algorithms are all within the "mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"""mml:mrow""mml:mo"±"/mml:mo""/mml:mrow""/mml:math"30cm, which meet the requirement of urban rail transit. Document type: Articl

Peripheral Leukocytapheresis Attenuates Acute Lung Injury Induced by Lipopolysaccharide In Vivo

The mortality of acute lung injury and acute respiratory distress syndrome (ALI/ARDS) remains high and efforts for prevention and treatments have shown little improvement over the past decades. The present study investigated the efficacy and mechanism of leukocytapheresis (LCAP) to partially eliminate peripheral neutrophils and attenuate lipopolysaccharide (LPS)-induced lung injury in dogs. A total of 24 healthy male mongrel dogs were enrolled and randomly divided into LPS, LCAP and LCAP-sham groups. All animals were injected with LPS to induce endotoxemia. The serum levels of leucocytes, neutrophil elastase, arterial blood gas, nuclear factor-kappa B (NF-κB) subunit p65 in lung tissues were measured. The histopathology and parenchyma apoptosis of lung tissues were examined. We found that 7, 3, and 7 animals in the LPS, LCAP, and sham-LCAP groups, respectively, developed ALI 36 h after LPS infusion. The levels of NF-κB p65 in lung tissue, neutrophils and elastase in blood, decreased significantly following LCAP. LCAP also alleviated apoptosis, and NF-κB p65 in lung tissues. Collectively, our results show that partial removal of leucocytes from peripheral blood decreases elastase level in serum. This, in turn, attenuates lung injuries and may potentially decrease the incidence of ALI

Construction and Validation of a Geometry-based Mathematical Model for Hard X-ray Imager

Quantitative and analytical analysis of modulation process of the collimator is a great challenge, and is also of great value to the design and development of Fourier transform imaging telescopes. The Hard X-ray Imager (HXI), as one of the three payloads onboard the Advanced Space-based Solar Observatory(ASO-S) mission, adopts modulating Fourier-Transformation imaging technique and will be used to explore mechanism of energy release and transmission in solar flare activities. In this paper, a mathematical model is developed to analyze the modulation function under a simplified condition first. Then its behavior under six degrees of freedom is calculated after adding the rotation matrix and translation change to the model. In addition, unparalleled light and extended sources also are considered so that our model can be used to analyze the X-ray beam experiment. Next, applied to the practical HXI conditions, the model has been confirmed not only by Geant4 simulations but also by some verification experiments. Furthermore, how this model will help to improve the image reconstruction process after the launch of ASO-S is also presented

Overlapping spectra resolution using nonnegative matrix factorization

Abstract Non-negative matrix factorization (NMF), with the constraints of non-negativity, has been recently proposed for multi-variate data analysis. Because it allows only additive, not subtractive, combinations of the original data, NMF is capable of producing region or parts-based representation of objects. It has been used for image analysis and text processing. Unlike PCA, the resolutions of NMF are non-negative and can be easily interpreted and understood directly. Due to multiple solutions, the original algorithm of NMF [D.D. Lee, H.S. Seung, Nature 401 (1999) 788] is not suitable for resolving chemical mixed signals. In reality, NMF has never been applied to resolving chemical mixed signals. It must be modified according to the characteristics of the chemical signals, such as smoothness of spectra, unimodality of chromatograms, sparseness of mass spectra, etc. We have used the modified NMF algorithm to narrow the feasible solution region for resolving chemical signals, and found that it could produce reasonable and acceptable results for certain experimental errors, especially for overlapping chromatograms and sparse mass spectra. Simulated two-dimensional (2-D) data and real GUJINGGONG alcohol liquor GC-MS data have been resolved soundly by NMF technique. Butyl caproate and its isomeric compound (butyric acid, hexyl ester) have been identified from the overlapping spectra. The result of NMF is preferable to that of Heuristic evolving latent projections (HELP). It shows that NMF is a promising chemometric resolution method for complex samples