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  • 1
    Language: English
    In: Nature communications, 2021-01-19, Vol.12 (1), p.450-450
    Description: Express delivery services are booming in both developed and emerging economies due to their low cost, convenience, and the fast growth in online shopping. The increasing environmental impacts of express delivery services and mitigation potentials, however, remain largely unexplored. Here we addressed such a gap for China, a country which is expanding online retail sales and express delivery rapidly. We found a total of 8.8 Mt of scrap packaging materials were generated by the express delivery sector in China in 2018. Its transportation-related GHG emissions surged from 0.3 Mt in 2007 to 13.7 Mt of CO -equivalent (CO e) in 2018, with an average of 0.27 kgCO e per piece. Over 80% from online shopping deliveries. We predict these emissions will reach 75 MtCO e by 2035. Nevertheless, it is possible to mitigate such GHG emissions by 102~134 MtCO e between 2020 and 2035 if a suite of policies is adopted, including a slowdown of delivery speed, fuel system upgrades, packaging materials reduction, logistics optimization, and carbon pricing.
    Subject(s): Index Medicus ; Environmental impact ; Sustainability
    E-ISSN: 2041-1723
    Source: Nature Open Access
    Source: PubMed Central
    Source: Directory of Open Access Journals
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 2
    Language: English
    In: IEEE geoscience and remote sensing letters, 2015-07, Vol.12 (7), p.1471-1475
    Description: In this letter, we propose a novel approach for improving Random Forest (RF) in hyperspectral image classification. The proposed approach combines the ensemble of features and the semisupervised feature extraction (SSFE) technique. The main contribution of our approach is to construct an ensemble of RF classifiers. In this way, the feature space is divided into several disjoint feature subspaces. Then, the feature subspaces induced by the SSFE technique are used as the input space to an RF classifier. This method is compared with a regular RF and an RF with the reduced features by the SSFE on two real hyperspectral data sets, showing an improved performance in ill-posed, poor-posed, and well-posed conditions. An additional study shows that the proposed method is less sensitive to the parameters.
    Subject(s): Radio frequency ; Training ; ensemble learning ; Accuracy ; semisupervised feature extraction (SSFE) ; Classification ; hyperspectral image ; Feature extraction ; Random Forest (RF) ; Hyperspectral imaging ; Engineering Sciences ; Signal and Image processing
    ISSN: 1545-598X
    E-ISSN: 1558-0571
    Source: IEEE Electronic Library (IEL)
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  • 3
    Language: English
    In: EPJ Web of conferences, 2020, Vol.225, p.1004
    Description: Recently, by using deep learning methods, a computer is able to surpass or come close to matching human performance on image analysis and recognition. This advanced methods could also help extracting features from neutron scattering experimental data. Those data contain rich scientific information about structure and dynamics of materials under investigation. Deep learning could help researchers better understand the link between experimental data and materials properties. Moreover,it could also help to optimize neutron scattering experiment by predicting the best possible instrument configuration. Among all possible experimental methods, we begin our study on the small-angle neutron scattering (SANS) data and by predicting the structure geometry of the sample material at an early stage. This step is a keystone to predict the experimental parameters to properly setup the instrument as well as the best measurement strategy. In this paper, we propose to use transfer learning to retrain a convolutional neural networks (CNNs) based pre rained model to adapt the scattering images classification, which could predict the structure of the materials at an early stage in the SANS experiment. This deep neural network is trained and validated on simulated database, and tested on real scattering images.
    Subject(s): Dynamic structural analysis ; Computer simulation ; Artificial neural networks ; Neutrons ; Object recognition ; Deep learning ; Image analysis ; Teaching methods ; Neutron scattering ; Neural networks ; Research methodology ; Human performance ; Machine learning ; Feature extraction ; Material properties ; Image classification ; deep learning ; transfer learning ; small-angle neutron scattering ; convolutional neural network ; artificial intelligence
    ISSN: 2100-014X
    ISSN: 2101-6275
    E-ISSN: 2100-014X
    Source: Academic Search Ultimate
    Source: Directory of Open Access Journals
    Source: ProQuest Central
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 4
    Language: English
    In: Insight (Northampton), 2015-08, Vol.57 (8), p.7
    Description: AStrion is an automatic spectrum analyser software, which proposes a new generic and data-driven method without any a priori information on the measured signals. In order to compute some general characteristics and derive the properties of the signal, the first step in this approach is to give some insight into the nature of the signal. This pre-analysis, the so-called data validation, contains a number of tests to reveal some of the properties and characteristics of the data, such as the acquisition validity (the absence of saturation and a posteriori in respect of the sampling theorem), the stationarity (or non-stationarity), the periodicity and the signal-to-noise ratio. Based on these characteristics, the proposed method defines indicators and alarm trigger thresholds and also categorises the signal into three classes, which helps to guide the following spectral analysis. The present paper introduces the four tests of this pre-analysis and the signal categorisation rules. Finally, the proposed approach is validated on a set of wind turbine vibration measurements to demonstrate its applicability for a long-term and continuous monitoring of real-world signals.
    Subject(s): Signal and Image Processing ; Computer Science
    ISSN: 1354-2575
    Source: Academic Search Ultimate
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  • 5
    Language: English
    In: Insight (Northampton), 2015-08-01, Vol.57 (8), p.457-463
    ISSN: 1354-2575
    Source: Academic Search Ultimate
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  • 6
    Conference Proceeding
    Conference Proceeding
    2012
    ISBN: 1467307912  ISBN: 9781467307918 
    ISSN: 2158-5873 
    Language: English
    In: 2012 13th International Workshop on Image Analysis for Multimedia Interactive Services, 2012-05, p.1-4
    Description: This paper presents an analysis of the effect of thirteen different kinds of sound on visual gaze when looking freely at videos to help to predict eye positions. First, an audio-visual experiment was designed with two groups of participants, with audio-visual (AV) and visual (V) conditions, to test the sound effect. Then, an audio experiment was designed to validate the classification of sound we proposed. We observed that the sound effect is different depending on the kind of sound, and that the classes with human voice (speech, singer, human noise and singers) have the greatest effect. Finally, a comparison of eye positions with a visual saliency model was carried out, which proves that adding sound to video decreases the accuracy of prediction of the visual saliency model.
    Subject(s): Visualization ; Accuracy ; Noise ; Humans ; Predictive models ; Speech ; Videos
    ISBN: 1467307912
    ISBN: 9781467307918
    ISSN: 2158-5873
    Source: IEEE Electronic Library (IEL)
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  • 7
    Language: English
    In: Nature medicine, 2013-01, Vol.19 (1), p.93-100
    Description: Emerging evidence indicates that microRNAs (miRNAs) have important roles in regulating osteogenic differentiation and bone formation. Thus far, no study has established the pathophysiological role for miRNAs identified in human osteoporotic bone specimens. Here we found that elevated miR-214 levels correlated with a lower degree of bone formation in bone specimens from aged patients with fractures. We also found that osteoblast-specific manipulation of miR-214 levels by miR-214 antagomir treatment in miR-214 transgenic, ovariectomized, or hindlimb-unloaded mice revealed an inhibitory role of miR-214 in regulating bone formation. Further, in vitro osteoblast activity and matrix mineralization were promoted by antagomir-214 and decreased by agomir-214, and miR-214 directly targeted ATF4 to inhibit osteoblast activity. These data suggest that miR-214 has a crucial role in suppressing bone formation and that miR-214 inhibition in osteoblasts may be a potential anabolic strategy for ameliorating osteoporosis.
    Subject(s): Cell Line ; Cell Proliferation ; Humans ; Mice, Inbred C57BL ; Male ; Mice, Transgenic ; MicroRNAs - metabolism ; Hindlimb Suspension ; Animals ; Activating Transcription Factor 4 - metabolism ; Bone and Bones - metabolism ; Female ; Osteoporosis - genetics ; Cell Differentiation ; Mice ; MicroRNAs - genetics ; Bone Remodeling - genetics ; Osteoblasts - metabolism ; Osteogenesis - genetics ; Physiological aspects ; Genetic aspects ; MicroRNA ; Bone regeneration ; Health aspects ; Index Medicus
    ISSN: 1078-8956
    E-ISSN: 1546-170X
    Source: Single Journals
    Source: Academic Search Ultimate
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 8
    Language: English
    In: Science China. Technological sciences, 2020-05-21, Vol.63 (10), p.2082-2097
    Description: Heavy commercial vehicles equipped with a hydraulic hub-motor auxiliary system (HHMAS) often operate under complex road conditions. Selecting appropriate operating mode and realizing reasonable energy management to match unpredictable road conditions are the keys to the driving performance and fuel economy of HHMAS. Therefore, a multi-mode energy management strategy (MM-EMS) based on improved global optimization algorithm is proposed in this study for HHMAS. First, an improved dynamic programming (DP) algorithm for HHMAS is developed. This improved DP algorithm considers the effect of SOC and vehicle speed, thereby preventing the calculation results from falling into local optimization. This algorithm also reduces the dimension of the control variable data grid, and the calculation time is reduced by 35% without affecting the accuracy. Second, a MM-EMS with hierarchical control is proposed. This strategy extracts the optimal control rules from the results of the improved DP algorithm. Then it divides the system’s operating region into two types, namely, single-mode working region and mixedmode working region. In the single-mode working region, mode switching is realized through fixed thresholds. In the mixedmode working region, a linear quadratic regulator (LQR) is adopted to determine a target mode and realize SOC tracking control. Finally, the designed MM-EMS is verified separately in offline simulation and hardware-in-the-loop (HIL) under actual vehicle test cycles. Simulation results show that the results between HIL and offline simulation are largely coincidence. Besides, in comparison with the engine optimal control strategy, the designed MM-EMS can achieve an approximate optimal control, with oil savings of 3.96%.
    Subject(s): Hardware-in-the-loop simulation ; Optimization algorithms ; Local optimization ; Global optimization ; Algorithms ; Simulation ; Mathematical analysis ; Traffic speed ; Optimal control ; Fuel economy ; Commercial vehicles ; Strategy ; Dynamic programming ; Tracking control ; Energy management ; Linear quadratic regulator
    ISSN: 1674-7321
    E-ISSN: 1869-1900
    Source: Alma/SFX Local Collection
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 9
    Language: English
    In: IEEE access, 2019, Vol.7, p.90666-90677
    Description: As energy storage device of hybrid electric vehicles (HEVs), ultracapacitors feature the advantages of higher power density and longer life cycle compared with batteries. However, when ultracapacitors of the same power level are used instead of batteries, fuel consumption becomes more sensitive to changes in control parameters as ultracapacitors store much less energy, and the state of charge (SOC) is power-sensitive. In this paper, optimization of control parameters for a power-split HEV with ultracapacitors is addressed to achieve better fuel economy. First, a power-split HEV model and a corresponding control strategy are established under the MATLAB script environment for convenient analysis and application of an optimization algorithm. Second, focusing on the power-sensitive characteristic of the SOC, three key control parameters are determined, and their effects on fuel consumption are analyzed. Third, an improved particle swarm optimization (IPSO) algorithm is proposed to overcome the disadvantage of the PSO trapping into "local optimum" and improve optimization efficiency. To fully exploit the fuel-saving capability of the HEV, driving cycle segmentation is also considered. The IPSO is used to optimize three key control parameters under the segmented adapted world transient vehicle cycle. Finally, the optimal results are applied to hardware-in-the-loop test to verify the effectiveness of the proposed optimization method. Compared with the fuel consumption before optimization, the fuel saving rate reaches 9.20% in the urban section, 6.40% in the roadway section, and 5.40% in freeway section.
    Subject(s): ultracapacitors ; Torque ; Supercapacitors ; Fuel economy ; power-split hybrid system ; improved particle swarm algorithm ; Mathematical model ; Engines ; Optimization ; Control strategy optimization
    ISSN: 2169-3536
    E-ISSN: 2169-3536
    Source: Directory of Open Access Journals
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  • 10
    Language: English
    In: Advances in mechanical engineering, 2018-04, Vol.10 (4), p.168781401876716
    Description: A novel Hydraulics AddiDrive System consisting of a variable displacement pump and two in-wheel motors is presented. The new Hydraulics AddiDrive System installed on the front axle of a traditional rear-wheel drive heavy vehicle aims to offer better mobility and improve traction efficiency in rough driving condition. To synchronize speeds between front in-wheel motors and rear wheels for optimal traction efficiency, a speed synchronization controller composed of feedforward and feedback control strategy is proposed for displacement adjustment of the variable displacement pump. The feedforward strategy is designed based on the relationship between the displacement coefficients of variable displacement pump and the gears. The feedback strategy utilizes proportional–integral algorithm to correct the dynamic errors. The compound speed synchronization controller helps to improve the control accuracy and adaptability under varied external conditions. Simulations are conducted on AMESim and MATLAB/Simulink to validate the proposed control strategy. The hardware-in-the-loop test allows for a more realistic evaluation of the proposed strategy, providing guidance of its application in real vehicle. Simulation and experiment results indicate that the maximum gradeability and traction force can be separately increased by 14.4%–17.2% and 13.4%–15.6% at low adhesion coefficient roads. The speed of front and rear wheel can be matched accurately with small difference below 1.31%.
    ISSN: 1687-8140
    E-ISSN: 1687-8140
    Source: Directory of Open Access Journals
    Source: Alma/SFX Local Collection
    Source: ProQuest Central
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