X射线显微术

综述了Ｘ射线显微术的基本原理，成像模式和技术及其在生命科学，材料科学等方面应用的新进展，同时简述了Ｘ线射显微术应用过程中应注意的几个问题。     

RPVC纱窗异型材定型模的设计

介绍ＲＰＶＣ纱窗异型材模具中定型模的设计及生产制造方法，该设计已通过实际生产检验。     

Angular multiplexed holographic memory system based on moving window on liquid crystal display and its crosstalk analysis

We suggested a new non-mechanical angular multiplexed holographic memory system using a moving window, which was fabricated by controlling the pixels of a liquid crystal display (LCD) electronically. If we use light passed through the window left and right, and up and down, each window makes a reference wave with a different incidence angle and we can store angular-multiplexed holograms. The feasibility of the proposed method was demonstrated through optical experiments and its crosstalk noise analysis was also presented.     

用GA算法解不同交货期窗口下的E/T调度问题

针对准时生产制下提前 /延迟 ( E/ T)费用的生产排序与调度问题 ,对不同交货期窗口下 E/ T指标的单机调度问题进行了分析 ,给出了在给定加工顺序条件下求解最优加工时间的动态规划算法。在此基础上 ,应用 GA( genetic al-gorithms)算法实现了求解。为提高算法优化性能 ,针对问题本身特性 ,分别从关键参数的选取 ;交叉操作的动态控制 ;变异操作的优化 3方面提出了相应改进策略。最后利用计算机仿真对算法性能进行研究 ,并得到一些经验性结论。仿真结果表明 ,该算法在优化性能和时间性能上均能满足工程上的要求。     

Knowledge discovery from communication network alarm databases

The technique of Knowlege Discovery in Databases (KDD) to learn valuable knowledge hidden in network alarm databases is introduced. To get such knowledge, we propose an efficient method based on sliding windows (named as Slidwin) to discover different episode rules from time squential alarm data. The experimental results show that given different thresholds parameters, large amount of different rules could be discovered quickly. Foundation item: Supported by the National 863 High-Tech Project (863-306-Z705-02) and National Natural Science Foundation of China (69896240) Biography: Wang Xi-miao (1973-), female, Ph. D. candidate. Research interests: network management and information processing.     

窗式空调器减振降噪研究

采用频谱分析法对ＫＣ－３２Ⅱ型单冷窗式空调器的主要振动和噪声源进行诊断，分析表明，空调器的薄板构件对振动、噪声有较大的影响。为此进行了阻尼处理，使振动量有较大的降低，噪声下降了２．０～２．５ｄＢ。     

金刚石近红外增透滤光保护窗口的制备及应用

报道金刚石近红外增透滤光保护窗口的研制及其在工业中的应用。采用热灯丝化学气相沉积方法成功地制备出金刚石近红外增透滤光保护窗口，其工作波长为１．３￣１．８μｍ，具有良好的光学透过性和优良的物理化学性质。该窗口已在上海宝钢获得成功应用。     

Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns

The traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper proposes status set sequential pattern mining with time windows (SSPMTW). In contrast to traditional methods, the item status is considered, and time windows, minimum confidence, minimum coverage, minimum factor set ratios and other constraints are added to mine more valuable rules in local time windows. The periodicity of these rules is also analyzed. According to the proposed method, this paper improves the Apriori algorithm, proposes the TW-Apriori algorithm, and explains the basic idea of the algorithm. Then, the feasibility, validity and efficiency of the proposed method and algorithm are verified by small-scale and large-scale examples. In a large-scale numerical example solution, the influence of various constraints on the mining results is analyzed. Finally, the solution results of SSPM and SSPMTW are compared and analyzed, and it is suggested that SSPMTW can excavate the laws existing in local time windows and analyze the periodicity of the laws, which solves the problem of SSPM ignoring the laws existing in local time windows and overcomes the limitations of traditional sequential pattern mining algorithms. In addition, the rules mined by SSPMTW reduce the entropy of the system.     

Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear

The increase in the proportion of elderly in Europe brings with it certain challenges that society needs to address, such as custodial care. We propose a scalable, easily modulated and live assistive technology system, based on a comfortable smart footwear capable of detecting walking behaviour, in order to prevent possible health problems in the elderly, facilitating their urban life as independently and safety as possible. This brings with it the challenge of handling the large amounts of data generated, transmitting and pre-processing that information and analysing it with the aim of obtaining useful information in real/near-real time. This is the basis of information theory. This work presents a complete system aiming at elderly people that can detect different user behaviours/events (sitting, standing without imbalance, standing with imbalance, walking, running, tripping) through information acquired from 20 types of sensor measurements (16 piezoelectric pressure sensors, one accelerometer returning reading for the 3 axis and one temperature sensor) and warn the relatives about possible risks in near-real time. For the detection of these events, a hierarchical structure of cascading binary models is designed and applied using artificial neural network (ANN) algorithms and deep learning techniques. The best models are achieved with convolutional layered ANN and multilayer perceptrons. The overall event detection performance achieves an average accuracy and area under the ROC curve of 0.84 and 0.96, respectively.