203 research outputs found
Consolidating Bus Charger Deployment and Fleet Management for Public Transit Electrification: A Life-Cycle Cost Analysis Framework
Despite rapid advances in urban transit electrification, the progress of systematic planning and management of the electric bus (EB) fleet is falling behind. In this research, the fundamental issues affecting the nascent EB system are first reviewed, including charging station deployment, battery sizing, bus scheduling, and life-cycle analysis. At present, EB systems are planned and operated in a sequential manner, with bus scheduling occurring after the bus fleet and infrastructure have been deployed, resulting in low resource utilization or waste. We propose a mixed-integer programming model to consolidate charging station deployment and bus fleet management with the lowest possible life-cycle costs (LCCs), consisting of ownership, operation, maintenance, and emissions expenses, thereby narrowing the gap between optimal planning and operations. A tailored branch-and-price approach is further introduced to reduce the computational effort required for finding optimal solutions. Analytical results of a real-world case show that, compared with the current bus operational strategies and charging station layout, the LCC of one bus line can be decreased significantly by 30.4%. The proposed research not only performs life-cycle analysis but also provides transport authorities and operators with reliable charger deployment and bus schedules for single- and multi-line services, both of which are critical requirements for decision support in future transit systems with high electrification penetration, helping to accelerate the transition to sustainable mobility
Optimal charging plan for electric bus considering time-of-day electricity tariff
Purpose: The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill. Design/methodology/approach: Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus. Findings: The electricity costs of the bus route can be reduced by applying the optimal charging plans. Originality/value: This paper produces a viable option for transit agencies to reduce their operation costs
Trip energy consumption estimation for electric buses
This study aims to develop a trip energy consumption (TEC) estimation model for the electric bus (EB) fleet planning, operation, and life-cycle assessment. Leveraging the vast variations of temperature in Jilin Province, China, real-world data of 31 EBs operating in 14 months were collected with temperatures fluctuating from −27.0 to 35.0 \ub0C. TEC of an EB was divided into two parts, which are the energy required by the traction and battery thermal management system, and the energy required by the air conditioner (AC) system operation, respectively. The former was regressed by a logarithmic linear model with ambient temperature, curb weight, travel distance, and trip travel time as contributing factors. The optimum working temperature and regression parameters were obtained by combining Fibonacci and Weighted Least Square. The latter was estimated by the operation time of the AC system in cooling mode or heating mode. Model evaluation and sensitivity analysis were conducted. The results show that: (i) the mean absolute percentage error (MAPE) of the proposed model is 12.108%; (ii) the estimation accuracy of the model has a probability of 99.7814% meeting the requirements of EB fleet scheduling; (iii) the MAPE has a 1.746% reduction if considering passengers’ boarding and alighting
Plant Phenotyping on Mobile Devices
Plants phenotyping is a fast and non-destructive method to obtain the physiological features of plants, compared with the expensive and time costing chemical analysis with plant sampling. Through plant phenotyping, scientists and farmers can tell plant health status more accurately compared to visual inspection, thus avoid the waste in time and resources and even to predict the productivity. However, the size and price of current plant phenotyping equipment restrict them from being widely applied at a farmer’s household level. Everyday field operation is barely achieved because of the availability of easy-to-carry and cost-effective equipment such as hyper-spectrum cameras, infrared cameras and thermal cameras. A plant phenotyping tool on mobile devices will make plant phenotyping technology more accessible to ordinary farmers and researchers. This application incorporates the use of physical optics, plant science models, and image processing ability of smartphones. With our special optical design, multispectral instead of RGB (red, green and blue) images can be obtained from the smartphones with fairly low cost. Through quick image processing on the smartphones, the APP will provide accurate plant physiological features predictions such as water, chlorophyll, and nitrogen. The sophisticated prediction models are applied which are provided by the Purdue’s plant phenotyping team. Once widely adopted, the information collected by the smartphones with the developed APP will be sent back to Purdue’s plant health big-data database. The feedback will not only allow us to improve our models, but also provide farmers and agricultural researchers easy access to real-time crop plant health data
Neural Super-Resolution for Real-time Rendering with Radiance Demodulation
Rendering high-resolution images in real-time applications (e.g., video
games, virtual reality) is time-consuming, thus super-resolution technology
becomes more and more crucial in real-time rendering. However, it is still
challenging to preserve sharp texture details, keep the temporal stability and
avoid the ghosting artifacts in the real-time rendering super-resolution. To
this end, we introduce radiance demodulation into real-time rendering
super-resolution, separating the rendered image or radiance into a lighting
component and a material component, due to the fact that the light component
tends to be smoother than the rendered image and the high-resolution material
component with detailed textures can be easily obtained. Therefore, we perform
the super-resolution only on the lighting component and re-modulate with the
high-resolution material component to obtain the final super-resolution image.
In this way, the texture details can be preserved much better. Then, we propose
a reliable warping module by explicitly pointing out the unreliable occluded
regions with a motion mask to remove the ghosting artifacts. We further enhance
the temporal stability by designing a frame-recurrent neural network to
aggregate the previous and current frames, which better captures the
spatial-temporal correlation between reconstructed frames. As a result, our
method is able to produce temporally stable results in real-time rendering with
high-quality details, even in the highly challenging 4 4
super-resolution scenarios
AMD-DBSCAN: An Adaptive Multi-density DBSCAN for datasets of extremely variable density
DBSCAN has been widely used in density-based clustering algorithms. However,
with the increasing demand for Multi-density clustering, previous traditional
DSBCAN can not have good clustering results on Multi-density datasets. In order
to address this problem, an adaptive Multi-density DBSCAN algorithm
(AMD-DBSCAN) is proposed in this paper. An improved parameter adaptation method
is proposed in AMD-DBSCAN to search for multiple parameter pairs (i.e., Eps and
MinPts), which are the key parameters to determine the clustering results and
performance, therefore allowing the model to be applied to Multi-density
datasets. Moreover, only one hyperparameter is required for AMD-DBSCAN to avoid
the complicated repetitive initialization operations. Furthermore, the variance
of the number of neighbors (VNN) is proposed to measure the difference in
density between each cluster. The experimental results show that our AMD-DBSCAN
reduces execution time by an average of 75% due to lower algorithm complexity
compared with the traditional adaptive algorithm. In addition, AMD-DBSCAN
improves accuracy by 24.7% on average over the state-of-the-art design on
Multi-density datasets of extremely variable density, while having no
performance loss in Single-density scenarios. Our code and datasets are
available at https://github.com/AlexandreWANG915/AMD-DBSCAN.Comment: Accepted at DSAA202
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