Evaluating Fuel Consumption for Continuous Descent Approach Based on QAR Data

Fuel savings are a significant aspect for evaluating the current and future technologies of civil aviation. Continuous-Descent Approach (CDA), as a representative of new concepts, requires a method for evaluating its fuel benefits. However, because of unavailability of the practical operational data, it is difficult to validate whether the previous fuel consumption mechanisms are suitable. This paper presents a unique method for quantifying potential fuel benefits. This permits an easy evaluation for the new procedures without modelling before implementing field tests. The proposed method is detailed in this paper. It derives from the inherent mechanical characteristic of aircraft engine, and utilizes historical flight data, rather than modelling, to predict fuel flow rates by matching flight conditions from Quick Access Recorder (QAR) data. The result has been shown to predict fuel consumption for conventional descent with the deviation of ±0.73%. To validate such method, a case study for our designed CDA procedure is presented. Fuel consumptions in baseline scenarios are estimated to analyse the variable impacts on fuel consumption. The estimated fuel benefits are consistent with the results in the previous field tests. This analysis helps support Air Traffic Management decisions on eventual field test by reducing the validation time and cost.</p

Preparation, characterization and application of a molecularly imprinted polymer for selective recognition of Sulpiride

A novel molecular imprinting polymer (MIP) was prepared by bulk polymerization using sulpiride as the template molecule, itaconic acid (ITA) as the functional monomer and ethylene glycol dimethacrylate (EGDMA) as the crosslinker. The formation of the MIP was determined as the molar ratio of sulpiride-ITA-EGDMA of 1:4:15 by single-factor experiments. The MIP showed good adsorption property with imprinting factor α of 5.36 and maximum adsorption capacity of 61.13 μmol/g, and was characterized by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR) and surface area analysis. With the structural analogs (amisulpride, tiapride, lidocaine and cisapride) and small molecules containing a mono-functional group (p-toluenesulfonamide, formamide and 1-methylpyrrolidine) as substrates, static adsorption, kinetic adsorption, and rebinding experiments were also performed to investigate the selective adsorption ability, kinetic characteristic, and recognition mechanism of the MIP. A serial study suggested that the highly selective recognition ability of the MIP mainly depended on binding sites provided by N-functional groups of amide and amine. Moreover, the MIP as solid-phase extractant was successfully applied to extraction of sulpiride from the mixed solution (consisted of p-toluenesulfonamide, sulfamethoxazole, sulfanilamide, p-nitroaniline, acetanilide and trimethoprim) and serum sample, and extraction recoveries ranged from 81.57% to 86.63%. The tentative tests of drug release in stimulated intestinal fluid (pH 6.8) demonstrated that the tablet with the MIP–sulpiride could obviously inhibit sulpiride release rate. Thus, ITA-based MIP is an efficient and promising alternative to solid-phase adsorbent for extraction of sulpiride and removal of interferences in biosample analysis, and could be used as a potential carrier for controlled drug releas

Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors

Neuromorphic vision sensors are bio-inspired cameras that naturally capture the dynamics of a scene with ultra-low latency, filtering out redundant information with low power consumption. Few works are addressing the object detection with this sensor. In this work, we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies. To make the best out of the event data, we introduce three different event-stream encoding methods based on Frequency, Surface of Active Event (SAE) and Leaky Integrate-and-Fire (LIF). We further integrate them into the state-of-the-art neural network architectures with two fusion approaches: the channel-level fusion of the raw feature space and decision-level fusion with the probability assignments. We present a qualitative and quantitative explanation why different encoding methods are chosen to evaluate the pedestrian detection and which method performs the best. We demonstrate the advantages of the decision-level fusion via leveraging multi-cue event information and show that our approach performs well on a self-annotated event-based pedestrian dataset with 8,736 event frames. This work paves the way of more fascinating perception applications with neuromorphic vision sensors

ORCHIDEE-MICT (revision 4126), a land surface model for the high-latitudes: model description and validation

Abstract. The high-latitude regions of the northern hemisphere are a nexus for the interaction between land surface physical properties and their exchange of carbon and energy with the atmosphere. At these latitudes, two carbon pools of planetary significance – those of the permanently frozen soils (permafrost), and of the great expanse of boreal forest – are vulnerable to destabilization in the face of currently observed climatic warming, the speed and intensity of which are expected to increase with time. Improved projections of future Arctic and boreal ecosystem transformation require improved land surface models that integrate processes specific to these cold biomes. To this end, this study lays out relevant new parameterizations in the ORCHIDEE-MICT land surface model. These describe the interactions between soil carbon, soil temperature and hydrology, and their resulting feedbacks on water and CO2 fluxes, in addition to a recently-developed fire module. Outputs from ORCHIDEE-MICT, when forced by two climate input data sets, are extensively evaluated against: (i) temperature gradients between the atmosphere and deep soils; (ii) the hydrological components comprising the water balance of the largest high-latitude basins, and (iii) CO2 flux and carbon stock observations. The model performance is good with respect to empirical data, despite a simulated excessive plant water stress and a positive land surface temperature bias. In addition, acute model sensitivity to the choice of input forcing data suggests that the calibration of model parameters is strongly forcing-dependent. Overall, we suggest that this new model design is at the forefront of current efforts to reliably estimate future perturbations to the high-latitude terrestrial environment. </jats:p

Tembusu Virus in Ducks, China

In China in 2010, a disease outbreak in egg-laying ducks was associated with a flavivirus. The virus was isolated and partially sequenced. The isolate exhibited 87%–91% identity with strains of Tembusu virus, a mosquito-borne flavivirus of the Ntaya virus group. These findings demonstrate emergence of Tembusu virus in ducks

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

Image Enhancement for Inspection of Cable Images Based on Retinex Theory and Fuzzy Enhancement Method in Wavelet Domain

Inspection images of power transmission line provide vision interaction for the operator and the environmental perception for the cable inspection robot (CIR). However, inspection images are always contaminated by severe outdoor working conditions such as uneven illumination, low contrast, and speckle noise. Therefore, this paper proposes a novel method based on Retinex and fuzzy enhancement to improve the image quality of the inspection images. A modified multi-scale Retinex (MSR) is proposed to compensate the uneven illumination by processing the low frequency components after wavelet decomposition. Besides, a fuzzy enhancement method is proposed to perfect the edge information and improve contrast by processing the high frequency components. A noise reduction procedure based on soft threshold is used to avoid the noise amplification. Experiments on the self-built standard test dataset show that the algorithm can improve the image quality by 3&ndash;4 times. Compared with several other methods, the experimental results demonstrate that the proposed method can obtain better enhancement performance with more homogeneous illumination and higher contrast. Further research will focus on improving the real-time performance and parameter adaptation of the algorithm

Heat dissipation design of end winding of permanent magnet synchronous motor for electric vehicle

With the continuous improvement of the requirements for motor accuracy, power and weight the heat of the motor is greatly increased while the heat dissipation space is becoming smaller and smaller. In this paper, using potting materials and heat pipes to build an efficient heat path from the coolant jackets to the winding head is to improve the cooling efficiency of the motor. Three motors are manufactured and installed the same casing outside the stator. The end windings of the motor A are directly exposed to air. The end windings of the motor B are filled with potting materials. The end windings of the motor C are installed with heat pipes and filled potting materials in gaps. Under the same heat source and cooling conditions, the final temperature rises of motor A, motor B and motor C are 114 °C, 98.4 °C and 91.6 °C respectively. The experimental results show that the design of improving the heat transfer at the ends of the coil reduces the motor temperature rising by 13.7%–19.6%

Research on the Region-Growing and Segmentation Technology of Micro-Particle Microscopic Images Based on Color Features

Silkworm microparticle disease is a legal quarantine standard in the detection of silkworm disease all over the world. The current common detection method, the Pasteur manual microscopy method, has a low detection efficiency all over the world. The low efficiency of the current Pasteur manual microscopy detection method makes the application of machine vision technology to detect microparticle spores an important technology to advance silkworm disease research. For the problems of the low contrast, different illumination conditions and complex image background of microscopic images of the ellipsoidal symmetrical shape of silkworm microparticle spores collected in the detection solution, a region growth segmentation method based on microparticle color and grayscale information is proposed. In this method, the fuzzy contrast enhancement algorithm is used to enhance the color information of micro-particles and improve the discrimination between the micro-particles and background. In the HSV color space with stable color, the color information of micro-particles is extracted as seed points to eliminate the influence of light and reduce the interference of impurities to locate the distribution area of micro-particles accurately. Combined with the neighborhood gamma transformation, the highlight feature of the micro-particle target in the grayscale image is enhanced for region growing. Mea6nwhile, the accurate and complete micro-particle target is segmented from the complex background, which reduces the background impurity segmentation caused by a single feature in the complex background. In order to evaluate the segmentation performance, we calculate the IOU of the microparticle sample image segmented by this method with its corresponding true value image, and the experiments show that the combination of color and grayscale features using the region growth technique can accurately and completely segment the microparticle target in complex backgrounds with a segmentation accuracy IOU as high as 83.1%

Research on the Region-Growing and Segmentation Technology of Micro-Particle Microscopic Images Based on Color Features

Silkworm microparticle disease is a legal quarantine standard in the detection of silkworm disease all over the world. The current common detection method, the Pasteur manual microscopy method, has a low detection efficiency all over the world. The low efficiency of the current Pasteur manual microscopy detection method makes the application of machine vision technology to detect microparticle spores an important technology to advance silkworm disease research. For the problems of the low contrast, different illumination conditions and complex image background of microscopic images of the ellipsoidal symmetrical shape of silkworm microparticle spores collected in the detection solution, a region growth segmentation method based on microparticle color and grayscale information is proposed. In this method, the fuzzy contrast enhancement algorithm is used to enhance the color information of micro-particles and improve the discrimination between the micro-particles and background. In the HSV color space with stable color, the color information of micro-particles is extracted as seed points to eliminate the influence of light and reduce the interference of impurities to locate the distribution area of micro-particles accurately. Combined with the neighborhood gamma transformation, the highlight feature of the micro-particle target in the grayscale image is enhanced for region growing. Mea6nwhile, the accurate and complete micro-particle target is segmented from the complex background, which reduces the background impurity segmentation caused by a single feature in the complex background. In order to evaluate the segmentation performance, we calculate the IOU of the microparticle sample image segmented by this method with its corresponding true value image, and the experiments show that the combination of color and grayscale features using the region growth technique can accurately and completely segment the microparticle target in complex backgrounds with a segmentation accuracy IOU as high as 83.1%