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| 1 | Refinement mechanism of cerium addition on solidification structure and sigma phase of super austenitic stainless steel S32654显示文摘The influence of Ce addition on the solidification structure and σphase of super austenitic stainless steel S32654 was systematically investigated via microstructural characterization and thermodynamic calcula- tion. The results indicate that a small addition of Ce could modify MgO and MnS into Ce-bearing inclu- sions Ce_(2)O_(3)and Ce_(2)O_(2)S. Ce addition led to noticeable refinement of both the dendrite structure and σphase. The refinement mechanism could be attributed to the combined actions of effective Ce-bearing inclusions and solute Ce. Effective Ce-bearing inclusions could serve as heterogeneous nucleation cores of austenite as well as σphase, which provided a favorable prerequisite for their refinement. Solute Ce significantly enhanced the undercooling degree of the system, further promoting dendrite structure re- finement. Meanwhile, solute Ce improved the eutectic precipitation conditions of σphase and further promoted its nucleation, while the dendrite refinement limited its growth space. Finally, more fine and dispersed σphase particles formed in S32654 with Ce addition. The refinement of dendrite structure and σ phase will reduce the temperature and time required for high-temperature homogenization, which is beneficial to the hot working of this steel. | Shucai Zhang Jiangtao Yu Huabing Li Zhouhua Jiang Yifeng Geng Hao Feng Binbin Zhang Hongchun Zhu | 2022 | Journal of Materials Science & Technology2022，，7： | 3 |
| 2 | Interfacial compatibility issues in rechargeable solid-state lithium metal batteries:a review显示文摘Solid-state lithium metal batteries(SSLBs)contain various kinds of interfaces,among which the solid electrode|solid electrolyte(ED|SE)interface plays a decisive role in the battery's power density and cycling stability.However,it is still lack of comprehensive knowledge and understanding about various interfacial physical/chemical processes so far.Although tremendous efforts have been dedicated to investigate the origin of large interfacial resistance and sluggish charge(electron/ion)transfer process,many scientific and technological challenges still remain to be clarified.In this review,we detach and discuss the critical individual challenge,including charge transfer process,chemical and electrochemical instability,space charge layers,physical contact and mechanical instability.The fundamental concepts,individual effects on the charge transfer and potential solutions are summarized based on material's thermodynamics,electrode kinetics and mechanical effects.It is anticipated that future research should focus on quantitative analysis,modeling analysis and in-situ microstructure characterizations in order to obtain an efficient manipulation about the complex interfacial behaviors in all solid-state Li batteries. | Hongchun Wang Jianping Zhu Yu Su Zhengliang Gong Yong Yang | 2021 | Science China Chemistry2021，64，6： | 3 |
| 3 | Landform classification based on optimal texture feature extraction from DEM data in Shandong Hilly Area, China显示文摘Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, and considerable research values are gained from texture feature extraction and analysis from DEM data. In this research, on the basis of optimal texture feature extraction, the hilly area in Shandong, China, was selected as the study area, and DEM data with a resolution of 500 m were used as the experimental data for landform classification. First, second-order texture measures and texture image were extracted from DEM data by using a gray level cooccurrence matrix (GLCM). Second, the variation characteristics of each texture measure were analyzed, and the optimal feature parameters, such as direction, gray level, and texture window, were determined. Meanwhile, the texture feature value, combined with maximum information, was calculated, and the multiband texture image was obtained by resolving three optimal texture measure images. Finally, a support vector machine (SVM) method was adopted to classify landforms on the basis of the multiband texture image. Results indicated that the texture features of DEM data can be sufficiently represented and measured via the quantitative GLCM method. However, the feature parameters during the texture feature value calculation required further optimization. Based on the image texture from DEM data, efficient classification accuracy and ideal classification effect were achieved. | Hongchun ZHU Yuexue XU Yu CHENG Haiying LIU Yipeng ZHAO | 2019 | Frontiers of Earth Science2019，13，3： | 1 |
| 4 | Transmission electron microscopy study of pseudoperiodically twinned Zn2SnO4 nanowires显示文摘 | Chen Hanyuan Wang Jianxiong Yu Hongchun | 2005 | J Phys Chem B2005，109，： | 1 |
| 5 | New generalized bipartite coherent-entangled state representation and its application显示文摘The new generalized coherent-entangled state representation |α, p μ,ν is successfully derived via constructing the integration of unity in normally ordered Gaussian operator forms and then decomposing it as projection operators. This is a convenient approach for obtaining new representations. We then prove that |α, p μ,ν has the completeness relation and only partly orthogonal, then discuss how to use a beamsplitter to produce such a state. As its potential application, formulas can be obtained by using the completeness of |α, p μ,ν , which is very important in mathematical physics, especially in quantum optics. | YU WenJian YUAN HongChun WANG JiSuo | 2011 | Science China(Physics,Mechanics & Astronomy)2011，54，12： | 1 |
| 6 | Deep learning of DEM image texture for landform classification in the Shandong area,China显示文摘Landforms are an important element of natural geographical environment,and textures are the research basis for the spatial differentiation,evolution features,and analysis rules of the landform.Using the regional difference of texture to describe the spatial distribution pattern of macro landform features is helpful to the landform classification and identification.Digital elevation model(DEM)image texture,which gives full expression to texture difference,is key data source to reflect the surface features and landform classification.Following the texture analysis,landform features analysis is assistant to different landforms classification,even in landform boundary.With the increasing accuracy requirement of landform information acquisition in geomorphic thematic mapping,hierarchical landform classification has become the focus and difficulty in research.Recently,the pattern recognition represented by Convolutional Neural Network has made great achievements in landform research,whose multichannel feature fusion structure satisfies the network structure of different landform classification.In this paper,DEM image texture was taken as the data source,and gray level co-occurrence matrix was applied to extract texture measures.Owing to the similarity of similar landform and the difference of different landform in a certain scale,a comprehensive texture factor reflecting landform features was proposed,and the spatial distribution pattern of landform features was systematically analyzed.On this basis,the coupling relationship between texture and landform type was explored.Thus,the deep learning method of Convolutional Neural Network is used to train the texture features,and the second-class landform classification is carried out through softmax.The classification results in small relief and mid-relief low mountains,overall accuracy are 84.35%and 69.95%respectively,while kappa coefficient are 0.72 and 0.40 respectively,were compared to that of traditional unsupervised landform classification results,and the superiority of Convolutional Neural Network classification was verified,it approximately improved 6%in overall accuracy and 0.4 in kappa coefficient. | Yuexue XU Hongchun ZHU Changyu HU Haiying LIU Yu CHENG | 2022 | Frontiers of Earth Science2022，16，2： | 0 |
| 7 | Classification and Summarization of Solar Irradiance and Power Forecasting Methods:A Thorough Review显示文摘Solar forecasting is of great importance for ensuring safe and stable operations of the power system with increased solar power integration,thus numerous models have been presented and reviewed to predict solar irradiance and power forecasting in the past decade.Nevertheless,few studies take into account the temporal and spatial resolutions along with specific characteristics of the models.Therefore,this paper aims to demonstrate a comprehensive and systematic review to further solve these problems.First,five classifications and seven pre-processing methods of solar forecasting data are systematically reviewed,which are significant in improving forecasting accuracy.Then,various methods utilized in solar irradiance and power forecasting are thoroughly summarized and discussed,in which 128 algorithms are elaborated in tables in the light of input variables,temporal resolution,spatial resolution,forecast variables,metrics,and characteristics for a more fair and comprehensive comparison.Moreover,they are categorized into four groups,namely,statistical,physical,hybrid,and others with relevant application conditions and features.Meanwhile,six categories,along with 30 evaluation criteria,are summarized to clarify the major purposes/applicability of the different methods.The prominent merit of this study is that a total of seven perspectives and trends for further research in solar forecasting are identified,which aim to help readers more effectively utilize these approaches for future in-depth research. | Bo Yang Tianjiao Zhu Pulin Cao Zhengxun Guo Chunyuan Zeng Danyang Li Yijun Chen Haoyin Ye Ruining Shao Hongchun Shu Tao Yu | 2023 | CSEE Journal of Power and Energy Systems2023，9，3： | 0 |
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