Effective overload control for large-scale online service system is crucial for protecting the system backend from overload. Conventionally, the design of overload control is ad-hoc for individual service. However, service-specific overload control could be detrimental to the overall system due to intricate service dependencies or flawed implementation of service. Service developers usually have difficulty to accurately estimate the dynamics of actual workload during the development of service. Therefore, it is essential to decouple the overload control from service logic. In this paper, we propose DAGOR, an overload control scheme designed for the account-oriented microservice architecture. DAGOR is service agnostic and system-centric. It manages overload at the microservice granule such that each microservice monitors its load status in real time and triggers load shedding in a collaborative manner among its relevant services when overload is detected. DAGOR has been used in the WeChat backend for five years. Experimental results show that DAGOR can benefit high success rate of service even when the system is experiencing overload, while ensuring fairness in the overload control. KEYWORDSoverload control, service admission control, microservice architecture, WeChat * Merged the errata published in https://arxiv.org/abs/1806.04075v2.
With the continuous promotion of electric vehicles, the pressure to scrap vehicle batteries is increasing, especially in China, where nickel cobalt manganese lithium (NCM) batteries have gradually come to occupy a dominant position in the battery market. In this study, we propose a two-stage pyrolysis process for vehicle batteries, which aims to effectively deal with the volatilization of organic solvents, the decomposition of lithium salts in the electrolyte and the removal of the separator material and polyvinylidene fluoride (PVDF) during battery recycling. By solving these issues, recycling is more effective, safe. Through thermogravimetric analysis (TGA), the pyrolysis characteristics of the battery’s internal materials are discussed, and 150 °C and 450 °C were determined as the pyrolysis temperatures of the two-stage pyrolysis process. The results show that in the first stage of pyrolysis, organic solvents EC (C4H3O3), DEC (C5H10O3) and EMC (C4H8O3) can be separated from the electrolyte. In the second stage, the pyrolysis can lead to the separator’s thermal decomposition. The gas products are alkane C2-C6, and the tar products are organic hydrocarbons C15-C36. Meanwhile, the solid residue of the battery’s internal material seems to be very homogeneous. Finally, the potential recovery value and pollution control countermeasures of the products and residues from the pyrolysis process are analyzed. Consequently, this method can effectively handle NCM vehicle battery recycling, which provides the basis for the subsequent hydrometallurgical or pyrometallurgical process for element recovery of the battery material.
Teeth detection and tooth segmentation are essential for processing Cone Beam Computed Tomography (CBCT) images. The accuracy decides the credibility of the subsequent applications, such as diagnosis, treatment plans in clinical practice or other research that is dependent on automatic dental identification. The main problems are complex noises and metal artefacts which would affect the accuracy of teeth detection and segmentation with traditional algorithms. In this study, we proposed a teeth-detection method to avoid the problems above and to accelerate the operation speed. In our method, (1) a Convolutional Neural Network (CNN) was employed to classify layer classes; (2) images were chosen to perform Region of Interest (ROI) cropping; (3) in ROI regions, we used a YOLO v3 and multi-level combined teeth detection method to locate each tooth bounding box; (4) we obtained tooth bounding boxes on all layers. We compared our method with a Faster R-CNN method which was commonly used in previous studies. The training and prediction time were shortened by 80% and 62% in our method, respectively. The Object Inclusion Ratio (OIR) metric of our method was 96.27%, while for the Faster R-CNN method, it was 91.40%. When testing images with severe noise or with different missing teeth, our method promises a stable result. In conclusion, our method of teeth detection on dental CBCT is practical and reliable for its high prediction speed and robust detection.
The main research direction for the disposal of spent lithium-ion batteries is focused on the recovery of precious metals. However, few studies exist on the recycling of LiFePO4 electric vehicle (EV) batteries because of their low recycling value. In addition, a detailed life cycle inventory (LCI) of waste plays a significant role in its life cycle assessment (LCA) for an environmental perspective. In this study, an end-of-life (EOL) LiFePO4 EV battery is disposed to achieve the LCI result. The approach comprises manual dismantling of the battery pack/module and crushing and pyrolysis of cells. The authors classify the dismantling results and use different disposal methods, such as recycling or incineration. Regarding the environmental emissions during pyrolysis, the authors record and evaluate the results according to the experimental data, the bill of materials (BOM), the mass conservation, and the chemical reaction equations. In addition, the electricity power demand is related to the electricity mix in China, and the waste gases and solid residue are treated by using neutralization and landfill, respectively. Finally, the authors integrate the LCI data with analysis data and a background database (Ecoinvent). After the integration of the total emission and consumption data, the authors obtained the total detailed LCI resulting from the disposal of the LiFePO4 vehicle battery. This LCI mainly includes the consumption of energy and materials, and emissions to air, water, and soil, which can provide the basis for the future LCA of LiFePO4 (LFP) batteries. Furthermore, the potential of industrial scale process research on the disposal of spent LiFePO4 batteries is discussed.
Abstract:The major factors affecting the popularization of electric vehicles (EV) are the limited travel range and the lack of charging infrastructure. Therefore, to further promote the penetration of EVs, it is of great importance to plan and construct more fast charging stations rationally. In this study, first we establish a travel pattern model based on the Monte Carlo simulation (MCS). Then, with the traveling data of EVs, we build a bi-level planning model of charging stations. For the upper model, with an aim to maximize the travel success ratio, we consider the influence of the placement of charging stations on the user's travel route. We adopt a hybrid method based on queuing theory and the greedy algorithm to determine the capacity of charging stations, and we utilize the total social cost and satisfaction index as two indicators to evaluate the optimal solutions obtained from the upper model. Additionally, the impact of the increase of EV ownership and slow charger coverage in the public parking lot on the fast charging demands and travel pattern of EV users are also studied. The example verifies the feasibility of the proposed method.
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