2#板坯连铸机振动台故障分析及改造

论述了银山型钢有限公司炼钢厂2#板坯连铸机自投产以来其振动台存在的问题，根据实际情况分析论证并进行改造实施，实施后效果明显。     

稠油注汽井用高温调剖封窜剂的研制与应用

随着单井蒸汽吞吐周期的增加，加剧了油层纵向和平面上的非均质性。汽驱井组油层存在高渗透带或孔道，注入蒸汽上升，从稠油顶部跨越，会出现越顶现象，造成提早汽窜，注蒸汽驱的波及效率进一步降低。针对此问题，开发了稠油注汽井用高温调剖封窜剂。室内研究结果表明，研制的高温调副封窜剂HD、HT具有较好的高温成胶性能和强度。现场应用孤东油田的9口井，有效8口，有效率88．9％，累计增油726．8t。     

为莱钢做强做大提供强有力的自动化信息化技术支撑

主要介绍了自动化部近几年来在科技创新和管理方面的工作经验,如围绕莱钢生产建设全力推进技术创新;以“数字莱钢”建设为目标,加快推进莱钢信息化建设,所有这些都为莱钢提供了强有力的自动化信息化技术支撑。     

Approximation algorithms for minimizing segments in radiation therapy

Intensity modulated radiation therapy (IMRT) is one of the most effective modalities for modern cancer treatment. The key to successful IMRT treatment hinges on the delivery of a two-dimensional discrete radiation intensity matrix using a device called a multileaf collimator (MLC). Mathematically, the delivery of an intensity matrix using an MLC can be viewed as the problem of representing a non-negative integral matrix (i.e., the intensity matrix) by a linear combination of certain special non-negative integral matrices called segments, where each such segment corresponds to one of the allowed states of the MLC. The problem of representing the intensity matrix with the minimum number of segments is known to be NP-complete. In this paper, we present two approximation algorithms for this matrix representation problem. To the best of our knowledge, these are the first algorithms to achieve non-trivial performance guarantees for multi-row intensity matrices.     

二碘化钐在分子内环化反应中的应用

综述了二碘化钐在分子内环化反应中的应用。     

温度,压力对漂移室气体增益的影响

研究了温度及气体压力对漂移室（工作于正比区）气体放大倍数的影响，温度和气体压力的变化范围分别是５￣４０℃及９０￣１０５ｋＰａ。用实验数据拟合得到了函数关系并测量了几种饱和度下函数中的参数值。     

HAR-sEMG: A Dataset for Human Activity Recognition on Lower-Limb sEMG

Knowledge and Information Systems - In the past decade, human activity recognition (HAR) has grown in popularity due to its applications in security and entertainment. As recent years have...     

MapReduce Workload Modeling with Statistical Approach

Large-scale data-intensive cloud computing with the MapReduce framework is becoming pervasive for the core business of many academic, government, and industrial organizations. Hadoop, a state-of-the-art open source project, is by far the most successful realization of MapReduce framework. While MapReduce is easy- to-use, efficient and reliable for data-intensive computations, the excessive configuration parameters in Hadoop impose unexpected challenges on running various workloads with a Hadoop cluster effectively. Consequently, developers who have less experience with the Hadoop configuration system may devote a significant effort to write an application with poor performance, either because they have no idea how these configurations would influence the performance, or because they are not even aware that these configurations exist. There is a pressing need for comprehensive analysis and performance modeling to ease MapReduce application development and guide performance optimization under different Hadoop configurations. In this paper, we propose a statistical analysis approach to identify the relationships among workload characteristics, Hadoop configurations and workload performance. We apply principal component analysis and cluster analysis to 45 different metrics, which derive relationships between workload characteristics and corresponding performance under different Hadoop configurations. Regression models are also constructed that attempt to predict the performance of various workloads under different Hadoop configurations. Several non-intuitive relationships between workload characteristics and performance are revealed through our analysis and the experimental results demonstrate that our regression models accurately predict the performance of MapReduce workloads under different Hadoop configurations.     

Low-Temperature Superplastic Deformation of Cold-Rolled Fe–5.6Mn–1.1Al–0.2C Steel

Metallurgical and Materials Transactions A - A cold-rolled low Al-added medium Mn steel was employed to investigate the low-temperature superplastic deformation at a relatively high initial strain...