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| 1 | 超级贝氏体钢中贝氏体转变的一种新的定量分析方法显示文摘用高温激光扫描共聚焦显微镜原位观察超级贝氏体钢中贝氏体的形核和生长。直接观察贝氏体转过程的形貌。而且做贝氏体转变的热模拟试验,用膨胀测量法记录。贝氏体钢的贝氏体转变过程在热机械拟器上用激光扫描共聚焦显微镜和膨胀测量法组合的新方法进行定量分析。 | Guang Xu Feng Liua Li Wang Haijiang Hu | 2013 | 热处理技术与装备2013，34，4： | 14 |
| 2 | Rock thin-section analysis and identification based on artificial intelligent technique显示文摘Rock thin-section identification is an indispensable geological exploration tool for understanding and recognizing the composition of the earth.It is also an important evaluation method for oil and gas exploration and development.It can be used to identify the petrological characteristics of reservoirs,determine the type of diagenesis,and distinguish the characteristics of reservoir space and pore structure.It is necessary to understand the physical properties and sedimentary environment of the reservoir,obtain the relevant parameters of the reservoir,formulate the oil and gas development plan,and reserve calculation.The traditional thin-section identification method has a history of more than one hundred years,which mainly depends on the geological experts'visual observation with the optical microscope,and is bothered by the problems of strong subjectivity,high dependence on experience,heavy workload,long identification cycle,and incapability to achieve complete and accurate quantification.In this paper,the models of particle segmentation,mineralogy identification,and pore type intelligent identification are constructed by using deep learning,computer vision,and other technologies,and the intelligent thinsection identification is realized.This paper overcomes the problem of multi-target recognition in the image sequence,constructs a fine-grained classification network under the multi-mode and multi-light source,and proposes a modeling scheme of data annotation while building models,forming a scientific,quantitative and efficient slice identification method.The experimental results and practical application results show that the thin-section intelligent identification technology proposed in this paper does not only greatly improves the identification efficiency,but also realizes the intuitive,accurate and quantitative identification results,which is a subversive innovation and change to the traditional thin-section identification practice. | He Liua Yi-Li Ren Xin Li Yan-Xu Hu Jian-Ping Wu Bin Li Lu Luo Zhi Tao Xi Liu Jia Liang Yun-Ying Zhang Xiao-Yu An Wen-Kai Fang | 2022 | Petroleum Science2022，19，4： | 4 |
| 3 | Harmonization of health data at national level:A pilot studyin China显示文摘 | Danhong Liua Xia Wang Feng Pan Peng Yang Yongyong Xu Xuejun Tang Jianping Hu Keqin Rao | | 0，，06： | 1 |
| 4 | On the isomerisation of cefozopran in solution 显示文摘 | Liua SY Zhang DS Hu CQ | 2010 | European J Medicinal Chemistry2010，45，12： | 1 |
| 5 | Annealing effects on electrical and optical properties of ZnO thin-film samples deposited by radio frequeney-magnetron sputtering on GaAs ( 001 ) substrates 显示文摘 | Liua H F Chua S J Hu G X | 2007 | JApplPhys2007，102，6： | 1 |
| 6 | Helicopter maritime search area planning based on a minimum bounding rectangle and K-means clustering显示文摘Helicopters are widely used in maritime Search and Rescue(SAR) missions. To ensure the success of SAR missions, search areas need to be carefully planned. With the development of computer technology and weather forecast technology, the survivors’ drift trajectories can be predicted more precisely, which strongly supports the planning of search areas for the rescue helicopter. However, the methods used to determine the search area based on the predicted drift trajectories are mainly derived from the continuous expansion of the area with the highest Probability of Containment(POC), which may lead to local optimal solutions and a decrease in the Probability of Success(POS), especially when there are several subareas with a high POC. To address this problem, this paper proposes a method based on a Minimum Bounding Rectangle and Kmeans clustering(MBRK). A silhouette coefficient is adopted to analyze the distribution of the survivors’ probable locations, which are divided into multiple clusters with K-means clustering. Then,probability maps are generated based on the minimum bounding rectangle of each cluster. By adding or subtracting one row or column of cells or shifting the planned search area, 12 search methods are used to generate the optimal search area starting from the cell with the highest POC in each probability map. Taking a real case as an example, the simulation experiment results show that the POS values obtained by the MBRK method are higher than those obtained by other methods,which proves that the MBRK method can effectively support the planning of search areas and that K-means clustering improves the POS of search plans. | Peisen XIONG Hu LIUa Yongliang TIAN Zikun CHEN Bin WANG Hao YANG | 2021 | Chinese Journal of Aeronautics2021，34，2： | 1 |
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