| 1. |
- Du, Haifeng, et al.
(författare)
-
Interaction of Individual Skyrmions in a Nanostructured Cubic Chiral Magnet
- 2018
-
Ingår i: Physical Review Letters. - : American Physical Society. - 0031-9007 .- 1079-7114. ; 120:19
-
Tidskriftsartikel (refereegranskat)abstract
- We report direct evidence of the field-dependent character of the interaction between individual magnetic skyrmions as well as between skyrmions and edges in B20-type FeGe nanostripes observed by means of high-resolution Lorentz transmission electron microscopy. It is shown that above certain critical values of an external magnetic field the character of such long-range skyrmion interactions changes from attraction to repulsion. Experimentally measured equilibrium inter-skyrmion and skyrmion-edge distances as a function of the applied magnetic field shows quantitative agreement with the results of micromagnetic simulations. The important role of demagnetizing fields and the internal symmetry of three-dimensional magnetic skyrmions are discussed in detail.
|
|
| 2. |
- Li, Kuijie, et al.
(författare)
-
Investigating the effect of packing format on LiNixCoyMnzO2 lithium-ion battery failure behavior based on multidimensional signals
- 2024
-
Ingår i: Journal of Power Sources. - : Elsevier. - 0378-7753 .- 1873-2755. ; 614
-
Tidskriftsartikel (refereegranskat)abstract
- Prismatic and pouch packaging formats are commonly used in LiNi x Co y Mn z O 2 (NCM) batteries for electric vehicles, each showing distinct failure dynamics. However, a comprehensive study is lacking on how these packaging types affect thermal runaway (TR) at the cell level and its propagation at the module level, with a particular gap in understanding the dynamics of multidimensional signals. In this study, we experimentally explore the effect of cell format on 40 Ah NCM523 prismatic and pouch battery failure behaviors under overcharging and overheating conditions, by applying multidimensional signals, including the swelling force, gas, voltage, and temperature of the batteries. Results indicate that both types of batteries exhibit similar time scales for the failure modes when overcharged. In contrast, under overheating conditions, the pouch batteries fail significantly earlier than the prismatic batteries, including abnormal swelling, venting, gas emission, internal short circuit, and TR. Additionally, the prismatic batteries can withstand a swelling force of 5000 N at venting, while it is 2000 N for the pouch batteries. During TR, the prismatic batteries present a maximum temperature increase rate below 100 K/s, while the pouch batteries exhibit one over 200 K/s. Furthermore, the pouch batteries generally display more severe TR hazards and faster TR propagation than the prismatic cells. This study enhances the comprehension of TR and TR propagation mechanisms across different cell formats, providing crucial insights for the safety design and early warning strategies of battery modules.
|
|
| 3. |
- Li, Xuebing, et al.
(författare)
-
A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion
- 2021
-
Ingår i: Measurement. - : Elsevier BV. - 0263-2241 .- 1873-412X. ; 185
-
Tidskriftsartikel (refereegranskat)abstract
- Tool wear monitoring during the cutting process is crucial for ensuring part quality and productivity. A datadriven monitoring approach based on radar map feature fusion is proposed for tool wear recognition and quantitative prediction, aiming at tracking the evolution of tool wear comprehensively. Specifically, the sensitive features from multi-source signals are fused by a radar map, and health indicators capable of characterizing the tool wear evolution are obtained. For the recognition of tool wear state and the quantitative prediction of tool wear values, the Adaboost Decision Tree (Adaboost-DT) ensemble learning model and stacked bi-directional long short-term memory (SBiLSTM) deep learning network are established, respectively. Experimental results demonstrated that the proposed approach could recognize the current wear state quickly and accurately whilst predicting wear values based on limited historical data available. Combining tool wear recognition and prediction results contributes to making a more flexible tool replacement decision in intelligent manufacturing processes.
|
|
| 4. |
- Niu, Guoxiang, et al.
(författare)
-
Subtropical forest macro-decomposers rapidly transfer litter carbon and nitrogen into soil mineral-associated organic matter
- 2024
-
Ingår i: Forest Ecosystems. - 2095-6355. ; 11
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Forest soils in tropical and subtropical areas store a significant amount of carbon. Recent frameworks to assess soil organic matter (SOM) dynamics under evolving global conditions suggest that dividing bulk SOM into particulate and mineral-associated organic matter (POM vs. MAOM) is a promising method for identifying how SOM contributes to reducing global warming. Soil macrofauna, earthworms, and millipedes have been found to play an important role in facilitating SOM processes. However, how these two co-existing macrofaunae impact the litter decomposition process and directly impact the formation of POM and MAOM remains unclear. Methods: Here, we set up a microcosm experiment, which consisted of 20 microcosms with four treatments: earthworm and litter addition (E), millipedes and litter addition (M), earthworm, millipedes, and litter addition (E+M), and control (only litter addition) in five replicates. The soil and litter were sterilized prior to beginning the incubation experiment to remove any existing microbes. After incubating the samples for 42 days, the litter properties (mass, C, and N contents), soil physicochemical properties, as well as the C and N contents, and POM and MAOM 13C abundance in the 0–5 and 5–10 cm soil layers were measured. Finally, the relative influences of soil physicochemical and microbial properties on the distribution of C and N in the soil fractions were analyzed. Results: The litter mass, C, and N associated with all four treatments significantly decreased after incubation, especially under treatment E+M (litter mass: −58.8%, litter C: −57.0%, litter N: −75.1%, respectively), while earthworm biomass significantly decreased under treatment E. Earthworm or millipede addition alone showed no significant effects on the organic carbon (OC) and total nitrogen (TN) content in the POM fraction, but joint addition of both significantly increased OC and TN regardless of soil depth. Importantly, all three macrofauna treatments increased the OC and TN content and decreased the 13C abundance in the MAOM fraction. More than 65% of the total variations in the distribution of OC and TN throughout the two fractions can be explained by a combination of soil physicochemical and microbial properties. Changes in the OC distribution in the 0–5 cm soil layer are likely due to a decrease in soil pH and an increase in arbuscular mycorrhizal fungi (AMF), while those in the 5–10 cm layer are probably caused by increases in soil exchangeable Ca and Mg, in addition to fungi and gram-negative (GN) bacteria. The observed TN distribution changes in the 0–5 cm soil likely resulted from a decrease in soil pH and increases in AMF, GN, and gram-negative (GP) bacteria, while TN distribution changes in the 5–10 cm soil could be explained by increases in exchangeable Mg and GN bacteria. Conclusions: The results indicate that the coexistence of earthworms and millipedes can accelerate the litter decomposition process and store more C in the MAOM fractions. This novel finding helps to unlock the processes by which complex SOM systems serve as C sinks in tropical forests and addresses the importance of soil macrofauna in maintaining C-neutral atmospheric conditions under global climate change.
|
|
| 5. |
- Zhang, Bowen, et al.
(författare)
-
An imbalanced data learning approach for tool wear monitoring based on data augmentation
- 2023
-
Ingår i: Journal of Intelligent Manufacturing. - : Springer Nature. - 0956-5515 .- 1572-8145.
-
Tidskriftsartikel (refereegranskat)abstract
- During cutting operations, tool condition monitoring (TCM) is essential for maintaining safety and cost optimization, especially in the accelerated tool wear phase. Due to the safety constraints of the actual production environment and the tool's properties, the data for each wear stage is usually unbalanced, and these unbalances lead to difficulties in failure monitoring. To this end, a novel TCM method based on data augmentation is proposed, which uses generative adversarial networks (GANs) to generate valuable artificial samples for a few classes to balance the data distribution. Unlike the traditional GANs, the proposed Conditional Wasserstein GAN-Gradient Penalty (CWGAN-GP) avoids pattern collapse and training instability and simultaneously generates more realistic data and signal samples with labels for different wear states. To evaluate the quality of the generated data, an evaluation index is proposed to evaluate the generated data while further screening the samples to achieve effective oversampling. Finally, the continuous wavelet transform (CWT) is combined with the convolutional neural network (CNN) architecture of Inception-ResNet-v2 for TCM, and it is demonstrated that data augmentation can effectively improve the performance of training classification models for unbalanced data by comparing three classification methods with two data augmentation experiments, and the proposed method has a better monitoring performance.
|
|
